5 year CPE Tranmission - Gene Intersection across species by chromosomes and plasmids

Ran the below commands in Unix shell

cd /data02/Analysis/Projects/2_CPE_Transmission/VennDiagram_of_genes_between_species/CPE_Transmission_Data_Analysis


#---------- Identity plasmids and chromosomes using PlasClass

time for j in $(ls *.fasta); do echo $j; time python3.7 ~/sw/PlasClass/classify_fasta.py -f $j -o "$j".plasclass.probs.out -p 60;  done
real    464m57.242s
user    592m31.525s
sys 133m29.767s

mkdir ../1_plasClass_Probs
mv *.plasclass.probs.out ../1_plasClass_Probs
$ awk '{print FILENAME "\t" $0}' *.out >CPE_Trans_PlasClass_Predictions_comb.tab



#---------- Identify Transposons using Bacant (Integron module is not working properly and Resistance genes are used from AMRfinderplus)
time for d in $(ls *.fasta);  do  
echo "$d";
bacant -n "$d" -o "$d"_bacant_out &
done

mkdir ../2_bacant_annot
mv *_bacant_out ../2_bacant_annot

awk '{$13=$14=""; print FILENAME "\t" $1 "\t" $2}' *_bacant_out/transposon.filter.tsv | fgrep -v 'QUERY' | sed 's/_bacant_out\/transposon.filter.tsv//g' | sed -e 's/_circ[0-9]*/_circ/g' -e 's/\(depth_[0-9]*.[0-9]*x\)[0-9]*/\1/g' | awk '{print $1 "\t" $2 "\t" $1"#"$2 "\t" $3}' >CPE_Trans_BacAnt_Transposons.tab 

awk '{print FILENAME "\t" $0}' *_bacant_out/integron.filter.tsv | fgrep -v 'QUERY' | sed 's/_bacant_out\/integron.filter.tsv//g' | sed 's/[0-9]*\~integron_..//g' | awk '{print $1 "\t" $2 "\t" $1"#"$2 "\t" $3}' >CPE_Trans_BacAnt_Integrons.tab

#---------- Identify IS using Prokka (NOTE: I used an updated ISdb (default ISdb for prokka) located in my desktop)
time awk '{print FILENAME "\t" $0}' *_prokka_out/*.gff | grep 'ISfinder'  | sed 's/ /_/g' | awk '{print $1 "\t" $2 "\t" $NF}' | sed -e 's/ID=.*ISfinder://g' -e 's/;locus_tag.*//g' -e 's/html.2020\/\///g' | sed 's/_prokka_out.*gff/_assembly_renamed.fasta/g' | awk '{print $1 "\t" $2 "\t" $1"#"$2 "\t" $3}' >../../VennDiagram_of_genes_between_species/CPE_Transmission_Data_Analysis/6_Prokka_InsertionSequences/CPE_Trans_Prokka_IS.tab 



#---------- Identity AMR genes using NCBI AMRfinderplus

# NOTE: Ran this earlier, so just copying the results

mkdir ../3_amrfinderplus_results 

#---------- Identity Virulence factor genes using Abricate

for d in $(ls *.fasta); do echo "abricate -db vfdb $d >"$d".vfdb.tab"; done >abricate_cmds.sh
time parallel --jobs 62 < abricate_cmds.sh &

mkdir ../4_abricate_vf_results 
mv *.vfdb.tab ../4_abricate_vf_results
cd ../4_abricate_vf_results

cat *_assembly_renamed.fasta.vfdb.tab | fgrep -v '#' | awk '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6}' >CPE_Trans_Abricate_VF_Genes.tab 


## Excluding possible contamination samples
-rwxrwxr-x 1 prakki prakki 9.8M Jul  6 14:17 batch12_27072019_ENT1410_assembly_renamed.fasta
-rwxrwxr-x 1 prakki prakki  11M Jul  6 14:17 batch2_21032019_ENT466_assembly_renamed.fasta
-rwxrwxr-x 1 prakki prakki  11M Jul  6 14:17 batch2_21032019_ENT497_assembly_renamed.fasta
-rwxrwxr-x 1 prakki prakki  11M Jul  6 14:18 batch2_21032019_ENT378_assembly_renamed.fasta
-rwxrwxr-x 1 prakki prakki  11M Jul  6 14:18 rbatch2_07082020_ENT792_assembly_renamed.fasta

# These assemblies have abnormal assembly sizes. So excluding them from analysis

## -- Reunning MLST 

for d in $(ls *.fasta); do echo "mlst $d >"$d".mlst.tab"; done >mlst_cmds.sh
time parallel --jobs 62 < mlst_cmds.sh &

Ran the below commands in R

library(dplyr)
library(UpSetR)
library(tidyverse)
library(splitstackshape)
library(reshape2)
library(randomcoloR)
library(ComplexHeatmap)
library(data.table)
library(kableExtra)
library(DT)
library(data.table)
library(formattable)




setwd("/data02/Analysis/Projects/2_CPE_Transmission/VennDiagram_of_genes_between_species/CPE_Transmission_Data_Analysis")


hybridAssemb_1198list <- readLines("/data02/Analysis/Projects/2_CPE_Transmission/VennDiagram_of_genes_between_species/CPE_Transmission_Data_Analysis/CPE_Hyrbid_Assemblies_1198.list")
#head(hybridAssemb_1198list)

###@@@@@@@@@@@@@@ Dataframe1 - PlasClass Predictions

plasClass_Prob_df <- data.table::fread(file = "1_plasClass_probs/CPE_Trans_PlasClass_Predictions_comb.tab", sep = "\t", header = FALSE)
#head(plasClass_Prob_df)

colnames(plasClass_Prob_df) <- c("Fasta", "Contig", "Probability")

plasClass_Prob_df <- 
  plasClass_Prob_df %>%  
  mutate(Fasta = stringr::str_replace(Fasta, ".plasclass.probs.out", "")) %>% 
  mutate(Fasta_Contig=paste(Fasta, Contig, sep = '#')) %>% 
  mutate(Classification = if_else(Probability >= 0.6, "Plasmid", "Chromosome")) 

#head(plasClass_Prob_df)

###@@@@@@@@@@@@@@ Dataframe2 - MLST
# Note: Based on discussion with  Dr. Ng, we will use Illumina Species and ST although for around 36 sample's Hyrbid assemblies, the species and ST is different from Illumina. This is done because we do not have to redo whole analysis because the 1071 plasmids are coming from 1198 hyrbid assemblies.

# The mlst_df in the below line contains Illumina species and ST
mlst_df <- data.table::fread(file = "5_MLST/CPE_Trans_1198_samples_Illumina_Species.txt", sep = "\t", header = TRUE, fill = TRUE) 
#head(mlst_df)
mlst_df <- mlst_df %>% filter(Fasta %in% hybridAssemb_1198list) # mlst_df now has only 1198 assembly information

Species_Counts <- as.data.frame(table(mlst_df$Species))
colnames(Species_Counts) <- c("Species","SampleCount")

Species_Counts %>% 
  kbl(caption = "Species and Sample Counts") %>%
  kable_classic(full_width = F, html_font = "Cambria")

Species and Sample Counts

Species	SampleCount
Citrobacter amalonaticus	10
Citrobacter farmeri	2
Citrobacter freundii	65
Citrobacter koseri	8
Citrobacter rodentium	2
Enterobacter aerogenes	15
Enterobacter cloacae	178
Escherichia coli	374
Klebsiella oxytoca	14
Klebsiella pneumoniae	521
Morganella morganii	4
Proteus mirabilis	4
Serratia marcescens	1

#head(Species_Counts)

#----------------Step1: Combining MLST and plasClass dataframes

plasClass_Prob_df <- left_join(plasClass_Prob_df, mlst_df, by = c("Fasta")) %>% 
    select(Fasta,Contig,Fasta_Contig,Classification,Species, ST) %>% 
    mutate(Species_ContigClass=paste(Species,Classification, sep = '#'))

#head(plasClass_Prob_df)

###@@@@@@@@@@@@@@ Dataframe2 - Bacant Predictions Transposons

bacAnt_df <- data.table::fread(file = "2_bacant_annot/CPE_Trans_BacAnt_Transposons.tab", sep = "\t", header = TRUE)
#head(bacAnt_df)

colnames(bacAnt_df) <- c("Fasta", "Contig", "Fasta_Contig", "Transposon")

#----------------Step2: Combining plasClass dataframe with bacant dataframe for plotting Transposons

bacant_plasClass_df <- left_join(bacAnt_df, plasClass_Prob_df, by = c("Fasta_Contig")) %>% 
  select(Fasta.x,Contig.x,Fasta_Contig,Transposon,Classification,Species_ContigClass)

#head(bacant_plasClass_df)

bacant_plasClass_df <- bacant_plasClass_df %>% filter(Fasta.x %in% hybridAssemb_1198list) # bacant_plasClass_df now has only 1198 assembly information

# bacant_plasClass_df %>%
#   select(Transposon,Species_ContigClass) %>%
#   table() %>% # frequency of unique transposons
#   kbl(caption = "Transposons and their counts in Chromosomes and Plasmids across Species") %>%
#   kable_classic(full_width = F, html_font = "Cambria")

customGreen0 = "#DeF7E9"
customRed = "#ff7f7f"

bacant_plasClass_df_total_col = bacant_plasClass_df %>%
  select(Fasta.x,Transposon,Species_ContigClass) %>%
  unique() %>%
  select(Species_ContigClass) %>% 
  unique() %>% 
  drop_na(Species_ContigClass) %>% count() %>% c()

Transposons

Table1

Percentages of Samples carrying transposons in Chromsomes and Plasmids across Species

# percentages of transposons (https://clarewest.github.io/blog/post/making-tables-shiny/)
bacant_plasClass_df %>%
  select(Fasta.x,Transposon,Species_ContigClass) %>%
  unique() %>%
  select(Transposon,Species_ContigClass) %>%
  count(Transposon, Species_ContigClass) %>%
  rename(Freq = n) %>%
  drop_na(Species_ContigClass) %>%
  mutate(Species_ContigClass2=Species_ContigClass) %>%
  separate(Species_ContigClass2, sep = "#", into = c("Species", "ContigClass")) %>%
  left_join(Species_Counts, by = c("Species")) %>%
  mutate(SamplePrcnt_wGeneElement=Freq*100/SampleCount) %>%
  select(Transposon,Species_ContigClass,SamplePrcnt_wGeneElement) %>%
  data.table::dcast(Transposon~Species_ContigClass, value.var="SamplePrcnt_wGeneElement") %>%
  replace(is.na(.), 0) %>%
  mutate_if(is.numeric, round, digits=1) %>% 
  formattable(., align = c("l",rep("c", bacant_plasClass_df_total_col$n)), 
              list(
    `Transposon` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
    area(col = 1:bacant_plasClass_df_total_col$n+1) ~ color_tile(customGreen0, customRed))) %>% 
  as.datatable(escape = FALSE,
               options = list(scrollX = FALSE, dom = 'lfti',
                              lengthMenu = list(c(5, 10, 25, 100, -1), c('5','10','25', '100', 'All')),
                              pageLength = 5),
               rownames = FALSE)

#head(bacant_plasClass_df)

Table2

Transposons and their unique counts in Chromsomes and Plasmids across Species

bacant_plasClass_df %>% 
  select(Transposon,Species_ContigClass) %>% 
  unique() %>% 
  table() %>% # frequency of unique transposons
  as.data.frame.matrix() %>% 
  kbl(caption = "Transposons and their unique counts in Chromsomes and Plasmids across Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria") 

Transposons and their unique counts in Chromsomes and Plasmids across Species

	Citrobacter amalonaticus#Chromosome	Citrobacter amalonaticus#Plasmid	Citrobacter farmeri#Plasmid	Citrobacter freundii#Chromosome	Citrobacter freundii#Plasmid	Citrobacter koseri#Plasmid	Citrobacter rodentium#Chromosome	Citrobacter rodentium#Plasmid	Enterobacter aerogenes#Chromosome	Enterobacter aerogenes#Plasmid	Enterobacter cloacae#Chromosome	Enterobacter cloacae#Plasmid	Escherichia coli#Chromosome	Escherichia coli#Plasmid	Klebsiella oxytoca#Chromosome	Klebsiella oxytoca#Plasmid	Klebsiella pneumoniae#Chromosome	Klebsiella pneumoniae#Plasmid	Morganella morganii#Chromosome	Morganella morganii#Plasmid	Proteus mirabilis#Chromosome	Proteus mirabilis#Plasmid	Serratia marcescens#Plasmid
Tn10|AP005147	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1	0	0	0	0
Tn1000|KX709966	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
Tn1721|X61367	0	0	0	1	1	0	0	1	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0
Tn2|KX242350	0	0	0	1	0	0	0	1	0	0	0	1	1	1	0	0	0	1	0	0	0	1	0
Tn2003|AY532647	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0
Tn2006|EF127491	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0
Tn2007|EF059914	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	1	0
Tn2008|GQ861438	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0
Tn2012|EU523120	1	1	0	1	1	1	0	1	0	1	1	1	1	1	0	1	1	1	1	0	0	0	0
Tn21	0	0	0	0	1	0	1	0	0	0	1	1	1	1	0	0	1	1	1	0	1	0	0
Tn21-like	0	1	0	1	1	0	0	0	0	0	1	1	1	1	0	0	1	1	1	0	1	0	0
Tn21|CP001182	0	1	0	1	1	0	1	0	0	1	0	1	1	1	0	0	1	1	1	0	0	0	0
Tn2555.3|AY485150.1	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
Tn3411|M19532	0	0	0	1	0	0	0	0	0	0	1	1	1	1	0	0	1	0	0	0	1	0	0
Tn3926|X78059	0	0	0	1	1	0	0	0	0	0	1	1	0	1	0	0	0	1	0	0	0	0	0
Tn4|HM749966	0	0	0	1	1	0	0	0	0	0	0	1	1	1	0	0	1	1	0	0	1	0	0
Tn402|KC999035	0	0	0	0	1	0	0	0	0	0	1	1	1	1	0	0	0	1	1	0	0	0	0
Tn4352|HQ840942	0	1	0	1	1	0	0	0	0	0	1	0	1	1	0	0	1	1	0	0	1	0	0
Tn4401a|KT378596	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
Tn4662a	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	0
Tn501|Z00027	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
Tn5036-like|GQ160960	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0	0	0
Tn5036|Y09025	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0	0	1	1	0	0	0	0	0
Tn5044/Tn5046	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	1	1	0	0	0	0	0
Tn5057|AJ302768	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	0	1	1	1	0	0	0	0
Tn5060|AJ551280	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Tn5073|AF461013	0	0	0	1	1	0	0	0	0	1	1	1	1	1	0	1	1	1	0	0	0	0	0
Tn5074|AF461012	0	0	0	0	0	0	0	1	0	0	1	1	1	0	0	0	0	1	1	0	0	0	0
Tn5075|AF457211	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
Tn511-3|EU287476	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
Tn512|EU306744	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0
Tn5393|M96392	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	1	0	0	0	0	0
Tn5393c|AF313472	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0
Tn5403|EU287476	0	1	1	1	1	1	0	1	0	1	1	1	1	1	0	1	1	1	0	1	0	0	0
Tn5718delta|AM261760	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	0	1	1	1	0	0	0	0
Tn6018-L|CP001182	0	0	0	1	1	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0
Tn602|AH000951	1	1	0	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0
Tn6022|CP001182	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0
Tn6029|HQ840942	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	1	0	0
Tn6082|AP005147	0	0	0	0	0	1	0	1	0	0	1	1	1	1	0	0	1	1	0	0	0	0	0
Tn6196|KC999035	0	0	0	1	1	0	0	0	0	1	1	1	1	1	1	0	1	1	0	0	0	0	1
Tn6205|CP003505	0	0	0	1	1	1	0	1	0	1	1	1	1	1	0	1	1	1	0	0	1	0	0
Tn6229|KF295829	0	0	0	0	1	0	0	0	0	0	1	1	0	0	0	0	1	1	0	0	0	0	0
Tn6238|KJ511462	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	1	1	0	0	0	0	0
Tn6292	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	1	1	1	0	0
Tn6934|CP046417.1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
Tn7-like|AM261760	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	1	0	0
Tn7|AP002527	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	1	0	0
Tn7051|MN915011.1	0	0	0	0	1	0	0	0	0	0	0	1	1	1	0	0	0	1	0	0	0	0	0
Tn801|AF080442	0	0	1	1	1	0	0	0	0	1	0	1	1	1	0	0	1	1	0	1	1	1	0
Tn9-like|HQ840942	0	0	0	1	0	0	0	0	0	1	1	1	1	1	0	0	1	1	1	0	1	0	0
Tn917|FJ208941	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0

Transposons UpsetR

UpsetR Plot Transposons

#----------------Step3: UpsetR Plot Transposons
 
tn_upsetR_df <- bacant_plasClass_df %>% select(Transposon,Species_ContigClass)
#View(tn_upsetR_df)
tn_upsetR_lt <- split(tn_upsetR_df$Transposon, tn_upsetR_df$Species_ContigClass) 
tn_comb_mat = make_comb_mat(tn_upsetR_lt)

#tn_comb_mat
#str(tn_comb_mat)

#as.data.frame(tn_comb_mat)
# comb_mat
# UpSet(tn_comb_mat)
# UpSet(t(comb_mat))
# UpSet(comb_mat, top_annotation = upset_top_annotation(comb_mat, add_numbers = TRUE),
# right_annotation = upset_right_annotation(comb_mat, add_numbers = TRUE))

 
col_size = comb_size(tn_comb_mat)
row_size = set_size(tn_comb_mat)

ht = UpSet(tn_comb_mat, 
      row_title = "Species#SeqClassification", column_title = "Transposon Intersection across species",
      pt_size = unit(6, "pt"),
      lwd = unit(2, "pt"),
      #comb_col = "red",
      # bg_col = c("#d6b23b","#81dce2","#acf49a","#a784d8","#acf49a","#81dce2","#1a51dd","#1d7a01","#683cd8","#d62c4c","#683cd8","#1d7a01" , "#dd71c0","#1a51dd","#dd71c0","#d62c4c"),
      #bg_col = upset_row_col$Color,
      top_annotation = upset_top_annotation(tn_comb_mat, gp = gpar(fill = "#009797"), add_numbers = FALSE, 
                                            bar_width = 0.5, annotation_name_rot = 90),
      right_annotation = upset_right_annotation(tn_comb_mat, add_numbers = TRUE, gp = gpar(fill = "#009797")),
      show_row_names = TRUE,
      row_names_gp = gpar(fontsize = 9), #changes font size of "set size" labels
      width = unit(700, units = "pt"), height = unit(10, "cm"))

ht = draw(ht)
#ht
col_od = column_order(ht)
row_od = row_order(ht)

decorate_annotation("intersection_size", {
  grid.text(col_size[col_od], 
            seq_len(length(col_size)), 
            unit(col_size[col_od], "native") + unit(2, "mm"), 
            default.units = "native", just = "bottom",
            gp = gpar(fontsize = 8))
})

Table4

Transposons proportions in Chromsome and plasmids by Species

bacant_TransposonCounts <- bacant_plasClass_df %>% 
  separate(Species_ContigClass, sep = "#", into = c("Species", "ContigClass")) %>%
  select(Species,Transposon,ContigClass) %>% 
  unique() %>% 
  mutate(Species_Transposon=paste(Species, Transposon, sep = '#')) %>% 
  select(Species_Transposon,ContigClass) %>% 
  table() %>% as.data.frame.matrix() %>% 
  mutate(BothChr_AND_Plasmid = case_when(Chromosome == 1 & Plasmid == 1 ~ 1, TRUE ~ 0)) %>% 
  tibble::rownames_to_column("Species#Transposon") %>% 
  separate("Species#Transposon", sep = "#", into = c("Species", "Transposon")) %>% 
  group_by(Species) %>% 
  summarise(Count_Transposon_in_Chr_old=sum(Chromosome), Count_Transposon_in_Plasmid_old=sum(Plasmid), Count_Transposon_in_Chr_Plasmid=sum(BothChr_AND_Plasmid)) %>% 
  mutate(Count_Transposon_in_Chr = Count_Transposon_in_Chr_old - Count_Transposon_in_Chr_Plasmid, 
         Count_Transposon_in_Plasmid = Count_Transposon_in_Plasmid_old - Count_Transposon_in_Chr_Plasmid) %>% 
  select(Species,Count_Transposon_in_Chr,Count_Transposon_in_Plasmid,Count_Transposon_in_Chr_Plasmid) %>% 
  column_to_rownames(var="Species")

# divides each cell value with corresponding row sum value
bacant_TransposonCounts_props <- bacant_TransposonCounts*100/rowSums(bacant_TransposonCounts) 

bacant_TransposonCounts_props %>% 
  kbl(caption = "Transposons proportions in Chromsome and plasmids by Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

Transposons proportions in Chromsome and plasmids by Species

	Count_Transposon_in_Chr	Count_Transposon_in_Plasmid	Count_Transposon_in_Chr_Plasmid
Citrobacter amalonaticus	0.000000	57.142857	42.857143
Citrobacter farmeri	0.000000	100.000000	0.000000
Citrobacter freundii	15.151515	36.363636	48.484848
Citrobacter koseri	0.000000	100.000000	0.000000
Citrobacter rodentium	18.181818	72.727273	9.090909
Enterobacter aerogenes	0.000000	80.000000	20.000000
Enterobacter cloacae	9.375000	31.250000	59.375000
Escherichia coli	13.513514	13.513514	72.972973
Klebsiella oxytoca	12.500000	75.000000	12.500000
Klebsiella pneumoniae	7.692308	30.769231	61.538461
Morganella morganii	66.666667	27.777778	5.555556
Proteus mirabilis	64.705882	5.882353	29.411765
Serratia marcescens	0.000000	100.000000	0.000000

Plot2

Transposons Percentages in Chromsome and plasmids by Species

bacant_TransposonCounts_props %>% 
  rownames_to_column(var="Species") %>% 
  reshape2::melt(variable = "Transposon location", value.name = "Percentage") %>% 
  na_if(0) %>% 
  ggplot(aes(x = Species, y=Percentage, fill = `Transposon location`, label = Percentage)) + 
  geom_bar(width = 0.4, position = position_stack(), stat = "identity")+
  scale_fill_manual(labels=c('Chromosome', 'Plasmid',"Both"), values = c("#fcb6b1", "#facc96", "#4dbfbc")) +
  geom_text(aes(label=paste0(sprintf("%1.1f", Percentage),"%")), position=position_stack(vjust=0.5)) +
  scale_x_discrete(expand = c(0,0)) +
  ylab('Percentage')+
  xlab('Species')+
  ggtitle('Percentages of Transposons by their location')+
  theme_classic() +
  theme(axis.text.y=element_text(size=16,  vjust = 0, hjust = 1),
        axis.text.x=element_text(size=16, angle=90, vjust = 0, hjust = 1.2),
        axis.line=element_blank(),
        axis.ticks=element_blank(),
        legend.position="top") + coord_flip()

Integrons

Table1

Percentages of Samples carrying Integrons in Chromsomes and Plasmids across Species

###@@@@@@@@@@@@@@ Dataframe2 - Bacant Predictions Integrons

bacAnt_Integrons_df <- data.table::fread(file = "2_bacant_annot/CPE_Trans_BacAnt_Integrons.tab", sep = "\t", header = TRUE)
#head(bacAnt_Integrons_df)

colnames(bacAnt_Integrons_df) <- c("Fasta", "Contig", "Fasta_Contig", "Integron")

#----------------Step2: Combining plasClass dataframe with bacant dataframe for plotting Transposons

bacAnt_Integrons_plasClass_df <- left_join(bacAnt_Integrons_df, plasClass_Prob_df, by = c("Fasta_Contig")) %>% 
  select(Fasta.x,Contig.x,Fasta_Contig,Integron,Classification,Species_ContigClass)

#head(bacAnt_Integrons_plasClass_df)

bacAnt_Integrons_plasClass_df <- bacAnt_Integrons_plasClass_df %>% filter(Fasta.x %in% hybridAssemb_1198list) # bacAnt_Integrons_plasClass_df now has only 1198 assembly information

# bacAnt_Integrons_plasClass_df %>%
#   select(Integron,Species_ContigClass) %>%
#   table() %>% # frequency of unique Integrons
#   kbl(caption = "Integrons and their counts in Chromsomes and Plasmids across Species") %>%
#   kable_classic(full_width = F, html_font = "Cambria")

bacAnt_Integrons_plasClass_df_total_col = bacAnt_Integrons_plasClass_df %>%
  select(Fasta.x,Integron,Species_ContigClass) %>%
  unique() %>%
  select(Species_ContigClass) %>% 
  unique() %>% 
  count() %>% c()

# percentages of Integrons
bacAnt_Integrons_plasClass_df %>% 
  select(Fasta.x,Integron,Species_ContigClass) %>%
  unique() %>% 
  select(Integron,Species_ContigClass) %>% 
  count(Integron, Species_ContigClass) %>% 
  rename(Freq = n) %>%  
  drop_na(Species_ContigClass) %>% 
  mutate(Species_ContigClass2=Species_ContigClass) %>% 
  separate(Species_ContigClass2, sep = "#", into = c("Species", "ContigClass")) %>% 
  left_join(Species_Counts, by = c("Species")) %>% 
  mutate(SamplePrcnt_wGeneElement=Freq*100/SampleCount) %>% 
  select(Integron,Species_ContigClass,SamplePrcnt_wGeneElement) %>% 
  data.table::dcast(Integron~Species_ContigClass, value.var="SamplePrcnt_wGeneElement") %>% 
  replace(is.na(.), 0) %>% 
  mutate_if(is.numeric, round, digits=1) %>% 
  #kbl(caption = "Sample count percentages for the Integrons in Chromsomes and Plasmids across Species") %>% 
  #kable_classic(full_width = F, html_font = "Cambria")
  formattable(., align = c("l",rep("c", bacAnt_Integrons_plasClass_df_total_col$n)), 
              list(
    `Transposon` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
    area(col = 1:bacAnt_Integrons_plasClass_df_total_col$n+1) ~ color_tile(customGreen0, customRed))) %>% 
  as.datatable(escape = FALSE,
               options = list(scrollX = FALSE, dom = 'lfti',
                              lengthMenu = list(c(5, 10, 25, 100, -1), c('5','10','25', '100', 'All')),
                              pageLength = 5),
               rownames = FALSE)

Table2

bacAnt_Integrons_plasClass_df %>% 
  select(Integron,Species_ContigClass) %>% 
  unique() %>% 
  table() %>% # frequency of unique Integrons
  kbl(caption = "Integrons and their unique counts in Chromsomes and Plasmids across Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

Integrons and their unique counts in Chromsomes and Plasmids across Species

	Citrobacter amalonaticus#Plasmid	Citrobacter farmeri#Plasmid	Citrobacter freundii#Chromosome	Citrobacter freundii#Plasmid	Citrobacter koseri#Plasmid	Citrobacter rodentium#Chromosome	Citrobacter rodentium#Plasmid	Enterobacter aerogenes#Plasmid	Enterobacter cloacae#Chromosome	Enterobacter cloacae#Plasmid	Escherichia coli#Chromosome	Escherichia coli#Plasmid	Klebsiella oxytoca#Plasmid	Klebsiella pneumoniae#Chromosome	Klebsiella pneumoniae#Plasmid	Morganella morganii#Chromosome	Morganella morganii#Plasmid	Proteus mirabilis#Chromosome	Serratia marcescens#Plasmid
In1000|KF921521	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
In1002|KF921555	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
In1003|KF921556	0	0	0	0	0	0	0	0	0	0	1	1	0	0	1	0	0	0	0
In1004|KF921558	0	0	0	0	0	0	0	0	0	0	1	1	0	0	1	0	0	0	0
In1015|KJ420612	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0
In104|AY463797	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
In1069|KM589497	0	0	0	1	0	0	0	0	0	1	1	0	0	1	1	0	0	0	0
In1114|KF914309	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0
In1133|KM595287	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
In1207|KP901091	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
In1227|KR699626	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
In1239|KT290951	0	0	0	0	0	0	0	0	1	0	0	1	0	0	1	0	1	0	0
In1249|KT316808	0	0	1	0	0	0	0	0	0	0	1	1	0	0	1	0	0	0	0
In1330|MF612148	0	0	0	1	0	0	0	0	0	0	1	1	0	1	1	0	0	0	0
In1368|KY047413	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
In1372|LC224310	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
In141|FJ711659	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0
In1411|KY426918	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0
In1440|LC318534	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	1	0
In1469|LC333379	0	0	0	1	0	0	0	0	0	1	0	0	0	1	1	0	0	0	0
In149|FJ457611	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0
In152|HQ386839	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
In153|JF701188	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
In193|DQ647028	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0
In2-10|KF188201	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0
In2-3|JX867127	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
In2-30|AB234887	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
In2-44|FJ591055	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
In206|FM957884	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0
In221|DQ372710	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
In240|EF368053	0	0	0	1	0	0	0	1	0	1	1	0	0	0	1	0	0	0	1
In244|KC543497	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
In261|FN824509	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
In262|FN823039	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
In282|AY522431	0	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	0	0	0
In29|AF329699	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
In366|AY069972	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
In384|FM877477	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
In404|DQ520934	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0
In415|EU934512	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
In440|AF227505	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0
In473|FJ854362	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0
In49|EU434618	0	0	0	1	0	0	0	0	0	1	0	0	0	1	1	0	0	0	0
In498|AY214164	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	1
In504|GU060321	0	0	0	0	0	0	0	0	1	0	1	1	0	0	1	0	0	0	0
In510|EU853659	1	0	0	1	0	0	0	1	1	1	1	1	0	1	1	0	0	0	0
In516|DQ323053	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
In538|KC200566	0	0	0	1	0	0	0	0	0	1	0	1	0	0	1	0	0	0	0
In54|AF220757	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0
In545|EU259884	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
In546|FJ594766	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
In554|AJ971343	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
In555|EU675686	0	0	0	1	0	0	0	0	0	1	0	1	0	0	1	0	0	0	0
In556|HQ170516	0	0	0	1	0	0	0	0	1	1	0	1	0	0	1	0	0	0	0
In570|AF453998	0	0	1	0	0	0	0	0	1	1	1	1	0	0	0	0	0	0	0
In573|AM237806	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0
In578|FN396877	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
In62|FJ594767	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
In653|HQ730120	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
In663|HQ875011	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0
In710|EU434603	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
In718|JF729199	0	0	0	0	0	0	0	0	0	0	1	1	0	0	1	0	0	0	0
In73|AF322577	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
In761|AF175203	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0
In771|JX131371	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
In784|JQ837988	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
In79|HQ730118	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
In792|JQ823014	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
In800|JX041889	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
In879|JX885645	0	0	1	0	0	0	0	0	0	0	1	1	0	0	1	0	0	0	0
In901|AY127872	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
In970|KF525327	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0

Integrons UpsetR

#----------------Step3: UpsetR Plot Integrons

integron_upsetR_df <- bacAnt_Integrons_plasClass_df %>% select(Integron,Species_ContigClass)
#View(integron_upsetR_df)
integron_upsetR_lt <- split(integron_upsetR_df$Integron, integron_upsetR_df$Species_ContigClass) 
integron_comb_mat = make_comb_mat(integron_upsetR_lt)

#integron_comb_mat
#str(integron_comb_mat)

#as.data.frame(integron_comb_mat)
# comb_mat
#UpSet(integron_comb_mat)


int_col_size = comb_size(integron_comb_mat)
int_row_size = set_size(integron_comb_mat)

integron_ht = UpSet(integron_comb_mat, 
           row_title = "Species#SeqClassification", column_title = "Integron Intersection across species",
           pt_size = unit(6, "pt"),
           lwd = unit(2, "pt"),
           #comb_col = "red",
#           bg_col = c("#d6b23b","#81dce2","#acf49a","#a784d8","#acf49a","#81dce2","#1a51dd","#1d7a01","#683cd8","#d62c4c","#683cd8","#1d7a01" , "#dd71c0","#1a51dd","#dd71c0","#d62c4c"),
           #bg_col = upset_row_col$Color,
           top_annotation = upset_top_annotation(integron_comb_mat, gp = gpar(fill = "#009797"), add_numbers = FALSE, 
                                                 bar_width = 0.5, annotation_name_rot = 90),
           right_annotation = upset_right_annotation(integron_comb_mat, add_numbers = TRUE, gp = gpar(fill = "#009797")),
           show_row_names = TRUE,
           row_names_gp = gpar(fontsize = 9), #changes font size of "set size" labels
           width = unit(700, units = "pt"), height = unit(10, "cm"))

ht = draw(integron_ht)
#ht
int_col_od = column_order(integron_ht)
int_row_od = row_order(integron_ht)

decorate_annotation("intersection_size", {
  grid.text(int_col_size[int_col_od], 
            seq_len(length(int_col_size)), 
            unit(int_col_size[int_col_od], "native") + unit(2, "mm"), 
            default.units = "native", just = "bottom",
            gp = gpar(fontsize = 8))
})

Table4

Integrons proportions in Chromsome and plasmids by Species

bacant_IntegronCounts <- bacAnt_Integrons_plasClass_df %>% 
  separate(Species_ContigClass, sep = "#", into = c("Species", "ContigClass")) %>%
  select(Species,Integron,ContigClass) %>% 
  unique() %>% 
  mutate(Species_Integron=paste(Species, Integron, sep = '#')) %>% 
  select(Species_Integron,ContigClass) %>% 
  table() %>% as.data.frame.matrix() %>% 
  mutate(BothChr_AND_Plasmid = case_when(Chromosome == 1 & Plasmid == 1 ~ 1, TRUE ~ 0)) %>% 
  tibble::rownames_to_column("Species#Integron") %>% 
  separate("Species#Integron", sep = "#", into = c("Species", "Integron")) %>% 
  group_by(Species) %>% 
  summarise(Count_Integron_in_Chr_old=sum(Chromosome), Count_Integron_in_Plasmid_old=sum(Plasmid), Count_Integron_in_Chr_Plasmid=sum(BothChr_AND_Plasmid)) %>% 
  mutate(Count_Integron_in_Chr = Count_Integron_in_Chr_old - Count_Integron_in_Chr_Plasmid, 
         Count_Integron_in_Plasmid = Count_Integron_in_Plasmid_old - Count_Integron_in_Chr_Plasmid) %>% 
  select(Species,Count_Integron_in_Chr,Count_Integron_in_Plasmid,Count_Integron_in_Chr_Plasmid) %>% 
  column_to_rownames(var="Species")

# divides each cell value with corresponding row sum value
bacant_IntegronCounts_props <- bacant_IntegronCounts*100/rowSums(bacant_IntegronCounts) 

bacant_IntegronCounts_props %>% 
  kbl(caption = "Integrons proportions in Chromsome and plasmids by Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

Integrons proportions in Chromsome and plasmids by Species

	Count_Integron_in_Chr	Count_Integron_in_Plasmid	Count_Integron_in_Chr_Plasmid
Citrobacter amalonaticus	0.000000	100.00000	0.00000
Citrobacter farmeri	0.000000	100.00000	0.00000
Citrobacter freundii	30.000000	65.00000	5.00000
Citrobacter koseri	0.000000	100.00000	0.00000
Citrobacter rodentium	0.000000	0.00000	100.00000
Enterobacter aerogenes	0.000000	100.00000	0.00000
Enterobacter cloacae	24.000000	60.00000	16.00000
Escherichia coli	20.000000	42.50000	37.50000
Klebsiella oxytoca	0.000000	100.00000	0.00000
Klebsiella pneumoniae	9.090909	77.27273	13.63636
Morganella morganii	66.666667	33.33333	0.00000
Proteus mirabilis	100.000000	0.00000	0.00000
Serratia marcescens	0.000000	100.00000	0.00000

Plot2

Integrons Percentages in Chromsome and plasmids by Species

bacant_IntegronCounts_props %>% 
  rownames_to_column(var="Species") %>% 
  reshape2::melt(variable = "Integron location", value.name = "Percentage") %>% 
  na_if(0) %>% 
  ggplot(aes(x = Species, y=Percentage, fill = `Integron location`, label = Percentage)) + 
  geom_bar(width = 0.4, position = position_stack(), stat = "identity")+
  scale_fill_manual(labels=c('Chromosome', 'Plasmid',"Both"), values = c("#fcb6b1", "#facc96", "#4dbfbc")) +
  geom_text(aes(label=paste0(sprintf("%1.1f", Percentage),"%")), position=position_stack(vjust=0.5)) +
  scale_x_discrete(expand = c(0,0)) +
  ylab('Percentage')+
  xlab('Species')+
  ggtitle('Percentages of Integrons by their location')+
  theme_classic() +
  theme(axis.text.y=element_text(size=16,  vjust = 0, hjust = 1),
        axis.text.x=element_text(size=16, angle=90, vjust = 0, hjust = 1.2),
        axis.line=element_blank(),
        axis.ticks=element_blank(),
        legend.position="top") + coord_flip()

AMR Genes

Table1

Percentages of Samples carrying AMR genes in Chromsomes and Plasmids across Species

###@@@@@@@@@@@@@@ Dataframe3 - AMRfinderplus - AMR Genes

amrFinderplus_df <- data.table::fread(file = "3_amrfinderplus_results/CPE_Trans_AMRFinderplus_AMR_Genes.tab", sep = "\t", header = TRUE)
#head(amrFinderplus_df)

colnames(amrFinderplus_df) <- c("Fasta", "Contig", "AMR_GENE", "AMR_GENE_CLASS","Coverage","Identity")

amrFinderplus_df <- amrFinderplus_df %>% filter(Fasta %in% hybridAssemb_1198list) # bacant_plasClass_df now has only 1198 assembly information

amrFinderplus_df <- 
  amrFinderplus_df %>%  
  mutate(Fasta_Contig=paste(Fasta, Contig, sep = '#'))

#----------------Step2: Combining plasClass dataframe with AMRFinderplus dataframe for plotting AMR genes

amrFinderplus_df <- left_join(amrFinderplus_df, plasClass_Prob_df, by = c("Fasta_Contig")) %>% 
  select(Fasta.x,Contig.x,AMR_GENE,AMR_GENE_CLASS,Classification,Species_ContigClass)

amrFinderplus_df_total_col = amrFinderplus_df %>%
  select(Fasta.x,AMR_GENE,Species_ContigClass) %>%
  unique() %>%
  select(Species_ContigClass) %>% 
  unique() %>% 
  count() %>% c()

# percentages of AMR_GENEs
amrFinderplus_df %>% 
  select(Fasta.x,AMR_GENE,Species_ContigClass) %>%
  unique() %>% 
  select(AMR_GENE,Species_ContigClass) %>% 
  count(AMR_GENE, Species_ContigClass) %>% 
  rename(Freq = n) %>%  
  drop_na(Species_ContigClass) %>% 
  mutate(Species_ContigClass2=Species_ContigClass) %>% 
  separate(Species_ContigClass2, sep = "#", into = c("Species", "ContigClass")) %>% 
  left_join(Species_Counts, by = c("Species")) %>% 
  mutate(SamplePrcnt_wGeneElement=Freq*100/SampleCount) %>% 
  select(AMR_GENE,Species_ContigClass,SamplePrcnt_wGeneElement) %>% 
  data.table::dcast(AMR_GENE~Species_ContigClass, value.var="SamplePrcnt_wGeneElement") %>% 
  replace(is.na(.), 0) %>% 
  mutate_if(is.numeric, round, digits=1) %>% 
  #kbl(caption = "Sample count percentages for the AMR genes in Chromsomes and Plasmids across Species") %>% 
  #kable_classic(full_width = F, html_font = "Cambria")
  formattable(., align = c("l",rep("c", amrFinderplus_df_total_col$n)), 
              list(
    `Transposon` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
    area(col = 1:amrFinderplus_df_total_col$n+1) ~ color_tile(customGreen0, customRed))) %>% 
  as.datatable(escape = FALSE,
               options = list(scrollX = FALSE, dom = 'lfti',
                              lengthMenu = list(c(5,10,25, 100, -1), c('5','10','25', '100', 'All')),
                              pageLength = 5),
               rownames = FALSE)

Table2

### AMR Genes and their unique counts in Chromsomes and Plasmids across Species

amrFinderplus_df %>% 
  select(AMR_GENE,Species_ContigClass) %>% 
  unique() %>% 
  table() %>% # frequency of unique transposons
  kbl(caption = "AMR Genes and their unique counts in Chromsomes and Plasmids across Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

AMR Genes and their unique counts in Chromsomes and Plasmids across Species

	Citrobacter amalonaticus#Chromosome	Citrobacter amalonaticus#Plasmid	Citrobacter farmeri#Plasmid	Citrobacter freundii#Chromosome	Citrobacter freundii#Plasmid	Citrobacter koseri#Chromosome	Citrobacter koseri#Plasmid	Citrobacter rodentium#Chromosome	Citrobacter rodentium#Plasmid	Enterobacter aerogenes#Chromosome	Enterobacter aerogenes#Plasmid	Enterobacter cloacae#Chromosome	Enterobacter cloacae#Plasmid	Escherichia coli#Chromosome	Escherichia coli#Plasmid	Klebsiella oxytoca#Chromosome	Klebsiella oxytoca#Plasmid	Klebsiella pneumoniae#Chromosome	Klebsiella pneumoniae#Plasmid	Morganella morganii#Chromosome	Morganella morganii#Plasmid	Proteus mirabilis#Chromosome	Proteus mirabilis#Plasmid	Serratia marcescens#Plasmid
aac(3)-IId	0	0	0	1	1	0	1	0	1	0	0	1	1	1	1	0	1	0	1	1	0	1	0	0
aac(3)-IIe	0	1	0	1	1	0	0	0	1	0	1	1	1	1	1	0	1	1	1	0	0	0	1	0
aac(3)-IIg	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
aac(3)-IVa	0	0	0	0	1	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0
aac(6’)-Ib	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	1	1	1	1	0	0	1
aac(6’)-Ib-cr5	0	1	1	1	1	0	1	0	1	0	1	1	1	1	1	0	1	1	1	0	0	0	0	0
aac(6’)-Ib’	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0
aac(6’)-Ib3	0	0	0	0	1	0	0	0	0	0	0	1	1	1	1	0	0	0	1	0	0	0	0	0
aac(6’)-Ib4	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0	0	1
aac(6’)-If	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
aac(6’)-IIc	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
aac(6’)-Il	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
aacA34	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
aadA1	0	0	0	1	1	0	0	0	0	0	1	1	1	1	1	0	0	1	1	0	0	1	0	0
aadA16	0	0	1	0	1	0	1	0	0	0	1	0	1	0	1	0	0	0	1	0	0	0	0	0
aadA2	0	1	0	0	1	0	0	1	0	0	1	1	1	1	1	0	0	1	1	0	0	0	0	0
aadA22	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
aadA5	0	0	0	1	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0	1	0	0
aadA8	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ampC	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ant(2’’)-Ia	0	0	0	1	1	0	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0	0	0
aph(3’’)-Ib	0	0	0	0	1	0	1	0	1	0	1	1	1	1	1	0	1	1	1	0	0	1	0	0
aph(3’)-Ia	0	1	0	0	1	0	0	0	0	0	0	1	0	1	1	0	0	1	1	0	0	1	0	0
aph(3’)-IIa	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0
aph(3’)-VI	0	0	0	0	1	0	0	0	0	0	0	1	1	1	1	0	0	0	1	0	0	0	0	0
aph(3’)-XV	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0
aph(4)-Ia	0	0	0	0	1	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0
aph(6)-Id	0	0	0	0	1	0	1	0	1	0	1	1	1	1	1	0	1	1	1	0	0	1	0	0
armA	0	0	0	0	1	0	1	0	0	0	1	0	1	1	1	0	1	1	1	0	0	0	0	0
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arr-2	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	0	0	1	1	0	0	0	0	0
arr-3	0	0	1	0	1	0	1	0	0	0	1	0	1	0	1	0	0	1	1	0	0	0	0	0
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blaACT-17	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
blaACT-24	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
blaACT-25	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
blaACT-28	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
blaACT-3	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
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blaACT-74	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
blaACT-90	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
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blaCMH-4	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-109	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-148	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
blaCMY-152	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-159	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
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blaCMY-4	0	0	0	0	0	0	1	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0
blaCMY-42	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
blaCMY-48	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-6	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0	0	0	0	0
blaCMY-65	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
blaCMY-66	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
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blaCMY-84	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
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blaSHV-186	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
blaSHV-187	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
blaSHV-26	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
blaSHV-27	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
blaSHV-28	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
blaSHV-2A	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
blaSHV-32	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
blaSHV-33	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
blaSHV-41	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
blaSHV-5	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	1	1	0	0	0	0	0
blaSHV-52	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
blaSHV-60	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
blaSHV-62	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
blaSHV-71	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
blaSHV-75	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
blaSHV-76	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
blaSHV-77	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
blaTEM-1	0	1	1	1	1	0	1	0	1	0	1	1	1	1	1	0	1	1	1	1	0	1	0	0
blaTEM-116	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0
blaTEM-135	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
blaTEM-169	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
blaTEM-176	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0
blaTEM-190	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
blaTEM-2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0
blaTEM-215	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0
blaTEM-30	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
blaTEM-31	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
blaTEM-32	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
blaVIM-4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
ble	0	1	1	0	1	0	1	0	1	0	1	1	1	1	1	0	1	1	1	0	1	0	0	0
catA1	0	0	0	1	0	0	0	0	0	0	1	1	1	1	1	0	0	0	1	1	0	1	0	0
catA2	0	0	0	1	1	0	0	0	0	0	0	1	1	0	1	0	0	0	1	0	0	0	0	0
catB	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	1	1	0	0	0	0	0
catB3	0	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	1	1	0	0	0	0	1
catB8	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
cmlA1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0
cmlA5	0	0	0	0	0	0	0	0	0	0	1	0	1	0	1	0	0	0	1	0	0	0	0	0
dfrA1	0	0	0	1	1	0	0	0	0	0	0	1	1	1	0	0	0	1	1	0	0	1	0	0
dfrA12	0	1	0	0	1	0	0	1	0	0	1	1	1	1	1	0	0	1	1	0	0	0	0	0
dfrA14	0	0	0	0	1	0	0	0	1	0	1	1	1	1	1	0	1	1	1	0	0	0	0	0
dfrA15	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	1	1	0	0	0	0
dfrA16	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
dfrA17	0	0	0	1	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0	1	0	0
dfrA23	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
dfrA27	0	0	1	0	1	0	1	0	0	0	1	0	1	0	1	0	0	0	1	0	0	0	0	0
dfrA29	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
dfrA32	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
dfrA33	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0
dfrA5	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0
dfrA7	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0	0
dfrB4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ere(A)	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0
erm(42)	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
floR	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0	0	0	0	0
fosA	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
fosA10	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0
fosA3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0
fosA4	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0
fosA7.5	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
lnu(F)	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0
mcr-1.1	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0	0	0	0	0	0	0
mcr-10.1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	1	1	0	0	0	0	0
mcr-3.1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
mcr-4.3	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
mef(B)	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0
mph(A)	0	1	1	0	1	0	1	1	0	0	1	1	1	1	1	0	1	1	1	0	1	1	0	0
mph(E)	0	0	0	0	1	0	1	0	0	0	1	0	1	1	1	0	1	1	1	0	0	0	0	0
msr(E)	0	0	0	0	1	0	1	0	0	0	1	0	1	1	1	0	1	1	1	0	0	0	0	0
oqxA	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
oqxA10	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
oqxA11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
oqxB	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
oqxB14	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
oqxB19	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
oqxB20	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
oqxB22	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
oqxB25	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
oqxB32	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
oqxB5	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
oqxB6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
qacEdelta1	0	1	1	1	1	0	1	1	0	0	1	1	1	1	1	0	0	1	1	1	1	1	0	1
qacG2	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1
qacL	0	0	0	1	1	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0	0	0	0	0
qepA1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
qepA4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
qepA8	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
qnrA1	0	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0	0	0	0	0
qnrB1	0	0	0	1	1	0	0	0	0	0	1	1	1	1	1	0	1	1	1	0	0	0	0	0
qnrB12	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB17	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB2	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
qnrB28	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB35	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB38	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB4	0	0	0	0	1	0	0	0	0	0	1	1	0	0	1	0	0	1	1	0	0	0	0	0
qnrB6	0	0	1	1	1	0	0	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0
qnrB69	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
qnrB7	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0
qnrB9	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0
qnrS1	1	0	0	0	1	0	0	0	1	0	1	0	1	1	1	0	0	0	1	0	0	0	0	0
qnrS2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
rmtB1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0
rmtC	0	0	0	0	0	0	0	0	1	0	0	1	1	0	1	0	0	0	1	0	0	0	0	0
rmtF1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	1	1	0	0	0	0	0
rmtF2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sat2	0	0	0	1	1	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	1	0	0
smr	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sul1	0	1	1	1	1	0	1	1	1	0	1	1	1	1	1	0	1	1	1	1	1	1	0	1
sul2	0	0	0	0	1	0	0	0	1	0	1	1	1	1	1	0	1	1	1	0	0	1	0	0
sul3	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0
tet(A)	0	0	1	0	1	0	0	0	1	0	1	1	1	1	1	0	1	1	1	1	0	1	0	0
tet(B)	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	1	0	0	0	0
tet(C)	0	0	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	0	1	0	0	0	0	0
tet(D)	0	0	0	0	1	0	0	0	0	0	1	0	1	0	0	0	0	1	1	0	0	0	0	0
tet(G)	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
tet(M)	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0
tet(X4)	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0

AMR Genes UpsetR

#----------------Step3: UpsetR Plot AMR Genes
amr_upsetR_df <- amrFinderplus_df %>% select(AMR_GENE,Species_ContigClass)

amr_upsetR_lt <- split(amr_upsetR_df$AMR_GENE, amr_upsetR_df$Species_ContigClass) 
amr_comb_mat = make_comb_mat(amr_upsetR_lt)

col_size = comb_size(amr_comb_mat)
row_size = set_size(amr_comb_mat)

amr_ht = UpSet(amr_comb_mat, 
           row_title = "Species#SeqClassification", column_title = "AMR Genes Intersection across species",
           pt_size = unit(6, "pt"),
           lwd = unit(2, "pt"),
           #comb_col = "red",
           # bg_col = c("#d6b23b","#81dce2","#acf49a","#a784d8","#acf49a","#81dce2","#1a51dd","#1d7a01","#683cd8","#d62c4c","#683cd8","#1d7a01" ,
           #            "#dd71c0","#1a51dd","#dd71c0","#d62c4c"),
           #bg_col = upset_row_col$Color,
           top_annotation = upset_top_annotation(amr_comb_mat, gp = gpar(fill = "#009797"), add_numbers = FALSE, 
                                                 bar_width = 0.5, annotation_name_rot = 90),
           right_annotation = upset_right_annotation(amr_comb_mat, add_numbers = TRUE, gp = gpar(fill = "#009797")),
           show_row_names = TRUE,
           row_names_gp = gpar(fontsize = 9), #changes font size of "set size" labels
           width = unit(900, units = "pt"), height = unit(12, "cm"))

amr_ht = draw(amr_ht)
#ht
col_od = column_order(amr_ht)
row_od = row_order(amr_ht)

decorate_annotation("intersection_size", {
  grid.text(col_size[col_od], 
            seq_len(length(col_size)), 
            unit(col_size[col_od], "native") + unit(2, "mm"), 
            default.units = "native", just = "bottom",
            gp = gpar(fontsize = 8))
})

Table3

AMR Genes counts in Chromsome and plasmids by Species

amrFinderplus_df %>% 
  separate(Species_ContigClass, sep = "#", into = c("Species", "ContigClass")) %>% 
  select(Species,AMR_GENE,ContigClass) %>% 
  unique() %>% 
  mutate(Species_AMR_GENE=paste(Species, AMR_GENE, sep = '#')) %>% 
  select(Species_AMR_GENE,ContigClass) %>% 
  table() %>% as.data.frame.matrix() %>% 
  mutate(BothChr_AND_Plasmid = case_when(Chromosome == 1 & Plasmid == 1 ~ 1, TRUE ~ 0)) %>% 
  tibble::rownames_to_column("Species#AMRGEnes") %>% 
  separate("Species#AMRGEnes", sep = "#", into = c("Species", "AMRGene")) %>% 
  group_by(Species) %>% 
  summarise(Count_AMRgenes_in_Chr_old=sum(Chromosome), Count_AMRgenes_in_Plasmid_old=sum(Plasmid), Count_AMRgenes_in_Chr_Plasmid=sum(BothChr_AND_Plasmid)) %>% 
  mutate(Count_AMRgenes_in_Chr = Count_AMRgenes_in_Chr_old - Count_AMRgenes_in_Chr_Plasmid, 
         Count_AMRgenes_in_Plasmid = Count_AMRgenes_in_Plasmid_old - Count_AMRgenes_in_Chr_Plasmid) %>% 
  select(Species,Count_AMRgenes_in_Chr,Count_AMRgenes_in_Plasmid,Count_AMRgenes_in_Chr_Plasmid) %>% 
  mutate(Total_AMRGenes = rowSums(.[2:4])) %>% 
  kbl(caption = "AMR Genes counts in Chromsome and plasmids by Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

AMR Genes counts in Chromsome and plasmids by Species

Species	Count_AMRgenes_in_Chr	Count_AMRgenes_in_Plasmid	Count_AMRgenes_in_Chr_Plasmid	Total_AMRGenes
Citrobacter amalonaticus	1	13	1	15
Citrobacter farmeri	0	14	0	14
Citrobacter freundii	24	47	16	87
Citrobacter koseri	2	20	0	22
Citrobacter rodentium	4	15	1	20
Enterobacter aerogenes	1	39	0	40
Enterobacter cloacae	30	47	30	107
Escherichia coli	11	57	52	120
Klebsiella oxytoca	3	20	0	23
Klebsiella pneumoniae	46	75	41	162
Morganella morganii	14	5	3	22
Proteus mirabilis	19	1	1	21
Serratia marcescens	0	8	0	8

Table4

AMR Genes proportions in Chromsome and plasmids by Species

amr_geneCounts <- amrFinderplus_df %>% 
  separate(Species_ContigClass, sep = "#", into = c("Species", "ContigClass")) %>% 
  select(Species,AMR_GENE,ContigClass) %>% 
  unique() %>% 
  mutate(Species_AMR_GENE=paste(Species, AMR_GENE, sep = '#')) %>% 
  select(Species_AMR_GENE,ContigClass) %>% 
  table() %>% as.data.frame.matrix() %>% 
  mutate(BothChr_AND_Plasmid = case_when(Chromosome == 1 & Plasmid == 1 ~ 1, TRUE ~ 0)) %>% 
  tibble::rownames_to_column("Species#AMRGEnes") %>% 
  separate("Species#AMRGEnes", sep = "#", into = c("Species", "AMRGene")) %>% 
  group_by(Species) %>% 
  summarise(Count_AMRgenes_in_Chr_old=sum(Chromosome), Count_AMRgenes_in_Plasmid_old=sum(Plasmid), Count_AMRgenes_in_Chr_Plasmid=sum(BothChr_AND_Plasmid)) %>% 
  mutate(Count_AMRgenes_in_Chr = Count_AMRgenes_in_Chr_old - Count_AMRgenes_in_Chr_Plasmid, 
         Count_AMRgenes_in_Plasmid = Count_AMRgenes_in_Plasmid_old - Count_AMRgenes_in_Chr_Plasmid) %>% 
  select(Species,Count_AMRgenes_in_Chr,Count_AMRgenes_in_Plasmid,Count_AMRgenes_in_Chr_Plasmid) %>% 
  column_to_rownames(var="Species")

# divides each cell value with corresponding row sum value
amr_geneCounts_props <- amr_geneCounts*100/rowSums(amr_geneCounts) 

amr_geneCounts_props %>% 
  kbl(caption = "AMR Genes proportions in Chromsome and plasmids by Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

AMR Genes proportions in Chromsome and plasmids by Species

	Count_AMRgenes_in_Chr	Count_AMRgenes_in_Plasmid	Count_AMRgenes_in_Chr_Plasmid
Citrobacter amalonaticus	6.666667	86.666667	6.666667
Citrobacter farmeri	0.000000	100.000000	0.000000
Citrobacter freundii	27.586207	54.022988	18.390805
Citrobacter koseri	9.090909	90.909091	0.000000
Citrobacter rodentium	20.000000	75.000000	5.000000
Enterobacter aerogenes	2.500000	97.500000	0.000000
Enterobacter cloacae	28.037383	43.925234	28.037383
Escherichia coli	9.166667	47.500000	43.333333
Klebsiella oxytoca	13.043478	86.956522	0.000000
Klebsiella pneumoniae	28.395062	46.296296	25.308642
Morganella morganii	63.636364	22.727273	13.636364
Proteus mirabilis	90.476191	4.761905	4.761905
Serratia marcescens	0.000000	100.000000	0.000000

Plot2

AMR Genes Percentages in Chromsome and plasmids by Species

amr_geneCounts_props %>% 
  rownames_to_column(var="Species") %>% 
  reshape2::melt(variable = "AMR gene location", value.name = "Percentage") %>% 
  na_if(0) %>% 
  ggplot(aes(x = Species, y=Percentage, fill = `AMR gene location`, label = Percentage)) + 
  geom_bar(width = 0.4, position = position_stack(), stat = "identity")+
  scale_fill_manual(labels=c('Chromosome', 'Plasmid',"Both"), values = c("#fcb6b1", "#facc96", "#4dbfbc")) +
  geom_text(aes(label=paste0(sprintf("%1.1f", Percentage),"%")), position=position_stack(vjust=0.5)) +
  scale_x_discrete(expand = c(0,0)) +
  ylab('Percentage')+
  xlab('Species')+
  ggtitle('Percentages of AMR genes by their location')+
  theme_classic() +
  theme(axis.text.y=element_text(size=16,  vjust = 0, hjust = 1),
        axis.text.x=element_text(size=16, angle=90, vjust = 0, hjust = 1.2),
        axis.line=element_blank(),
        axis.ticks=element_blank(),
        legend.position="top") + coord_flip()

VF Genes

Table1

Percentages of Samples carrying Virulence genes in Chromsomes and Plasmids across Species

###@@@@@@@@@@@@@@ Dataframe4 - Abricate - Virulence Factor Genes

Abricate_vf_df <- data.table::fread(file = "4_abricate_vf_results/CPE_Trans_Abricate_VF_Genes.tab", sep = "\t", header = TRUE)
#head(Abricate_vf_df)

##########-------------- Modifying dataframe START -------------------################

colnames(Abricate_vf_df ) <- c("Fasta", "Contig","START","END","STRAND","VF_GENE")

Abricate_vf_df <- 
  Abricate_vf_df %>%  
  mutate(Fasta_Contig=paste(Fasta, Contig, sep = '#'))

Abricate_vf_df <- Abricate_vf_df %>% filter(Fasta %in% hybridAssemb_1198list) # bacant_plasClass_df now has only 1198 assembly information

#head(Abricate_vf_df)

#----------------Step2: Combining plasClass dataframe with abricate_vf_dataframe for plotting virulence genes

Abricate_vf_df <- left_join(Abricate_vf_df, plasClass_Prob_df, by = c("Fasta_Contig")) %>% 
  select(Fasta.x,Contig.x,VF_GENE,Classification,Species_ContigClass)

#View(Abricate_vf_df)

Abricate_vf_df_total_col = Abricate_vf_df %>%
  select(Fasta.x,VF_GENE,Species_ContigClass) %>%
  unique() %>%
  select(Species_ContigClass) %>% 
  unique() %>% 
  drop_na(Species_ContigClass) %>% count() %>% c()

# percentages of AMR_GENEs
Abricate_vf_df %>% 
  select(Fasta.x,VF_GENE,Species_ContigClass) %>%
  unique() %>% 
  select(VF_GENE,Species_ContigClass) %>% 
  count(VF_GENE, Species_ContigClass) %>% 
  rename(Freq = n) %>%  
  drop_na(Species_ContigClass) %>% 
  mutate(Species_ContigClass2=Species_ContigClass) %>% 
  separate(Species_ContigClass2, sep = "#", into = c("Species", "ContigClass")) %>% 
  left_join(Species_Counts, by = c("Species")) %>% 
  mutate(SamplePrcnt_wGeneElement=Freq*100/SampleCount) %>% 
  select(VF_GENE,Species_ContigClass,SamplePrcnt_wGeneElement) %>% 
  data.table::dcast(VF_GENE~Species_ContigClass, value.var="SamplePrcnt_wGeneElement") %>% 
  replace(is.na(.), 0) %>% 
  mutate_if(is.numeric, round, digits=1) %>% 
 # kbl(caption = "Sample count percentages for the Virulence factor genes in Chromsomes and Plasmids across Species") %>% 
 # kable_classic(full_width = F, html_font = "Cambria")
  formattable(., align = c("l",rep("c", Abricate_vf_df_total_col $n)), 
              list(
    `Transposon` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
    area(col = 1:Abricate_vf_df_total_col $n+1) ~ color_tile(customGreen0, customRed))) %>% 
  as.datatable(escape = FALSE,
               options = list(scrollX = FALSE, dom = 'lfti',
                              lengthMenu = list(c(5, 10, 25, 100, -1), c('5','10','25', '100', 'All')),
                              pageLength = 5),
               rownames = FALSE)

Table2

Virulence genes and their unique counts in Chromsomes and Plasmids across Species

Abricate_vf_df %>% 
  select(VF_GENE,Species_ContigClass) %>% 
  unique() %>% 
  table() %>% # frequency of unique transposons
  kbl(caption = "Virulence factors genes and their unique counts in Chromsomes and Plasmids across Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

Virulence factors genes and their unique counts in Chromsomes and Plasmids across Species

	Citrobacter amalonaticus#Chromosome	Citrobacter farmeri#Chromosome	Citrobacter freundii#Chromosome	Citrobacter freundii#Plasmid	Citrobacter koseri#Chromosome	Citrobacter rodentium#Chromosome	Enterobacter aerogenes#Chromosome	Enterobacter cloacae#Chromosome	Enterobacter cloacae#Plasmid	Escherichia coli#Chromosome	Escherichia coli#Plasmid	Klebsiella oxytoca#Chromosome	Klebsiella pneumoniae#Chromosome	Klebsiella pneumoniae#Plasmid	Serratia marcescens#Chromosome
aap/aspU	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
afaA	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
afaB-I	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
afaC-I	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
afaD	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
afaE-I	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
aslA	0	0	1	0	0	0	0	1	0	1	0	0	1	0	0
astA	0	0	1	0	0	0	0	0	1	1	1	1	1	1	0
cesAB	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
cesD	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
cesD2	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
cesL	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
cesT	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
cheY	0	0	1	0	0	0	0	0	0	0	0	0	0	0	1
chuA	0	0	0	0	1	0	0	1	0	1	0	0	0	0	0
chuS	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
chuT	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
chuU	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
chuV	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
chuW	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
chuX	0	0	1	0	1	0	0	1	0	1	0	0	0	0	0
chuY	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
cnf1	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0
csgA	1	1	0	0	0	1	0	0	0	0	0	0	0	0	0
csgB	1	1	1	0	1	1	0	1	0	1	0	0	0	0	0
csgD	1	1	1	0	1	1	0	1	0	1	0	0	0	0	0
csgE	1	1	1	0	1	1	0	0	0	0	0	0	0	0	0
csgF	1	1	1	0	1	1	0	1	0	1	0	0	0	0	0
csgG	1	1	1	0	1	1	0	1	0	1	0	0	0	0	0
csnA	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
daaD	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
daaE	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
daaF	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
draA	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
draD	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
draE2	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
draP	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
eae	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
east1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
entA	1	0	1	0	1	0	1	1	0	1	0	1	1	0	0
entB	1	1	1	0	1	1	1	1	0	1	0	1	1	0	0
entC	0	0	1	0	0	1	0	1	0	1	0	0	0	0	0
entD	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
entE	1	0	1	0	1	1	0	1	0	1	0	0	0	0	0
entF	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
entS	1	1	1	0	1	0	0	1	0	1	0	0	0	0	0
escC	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
escD	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
escE	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
escF	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
escG	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
escI	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
escJ	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
escL	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
escN	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
escO	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
escP	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
escR	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
escS	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
escT	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
escU	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
escV	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
espA	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
espB	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
espD	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
espG	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
espK	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
espL1	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
espL4	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
espR1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
espR3	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
espR4	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
espX1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
espX2	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
espX4	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
espX5	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
espY1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
espY2	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
espY3	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
espY4	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
etgA	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
faeC	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
faeD	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
faeE	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
faeF	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
faeH	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
faeI	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
fdeC	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
fepA	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
fepB	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
fepC	1	1	1	0	1	1	1	1	0	1	0	0	1	0	0
fepD	1	0	1	0	0	1	0	1	0	1	0	0	1	0	0
fepG	1	1	1	0	1	1	0	1	0	1	0	0	1	0	0
fes	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
fimA	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
fimB	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
fimC	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
fimD	0	0	1	0	1	0	0	1	0	1	0	0	0	0	0
fimE	0	1	0	0	0	0	0	1	0	1	0	0	0	0	0
fimF	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
fimG	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
fimH	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
fimI	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
flgH	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0
fliG	0	0	1	0	1	0	1	0	0	1	0	0	0	0	1
fliM	0	0	0	0	0	0	1	0	0	1	0	0	0	0	1
fliN	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0
fliP	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1
focA	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
focC	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
focD	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
focF	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
focG	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
focH	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
fyuA	1	0	1	0	1	0	1	1	0	1	0	1	1	1	0
gspC	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
gspD	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
gspE	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
gspF	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
gspG	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
gspH	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
gspI	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
gspJ	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
gspK	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
gspL	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
gspM	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
gtrA	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
gtrB	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
hlyA	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0
hlyB	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0
hlyC	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0
hlyD	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0
ibeA	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
iroB	0	0	0	0	0	0	1	1	0	1	1	0	0	1	0
iroC	0	0	0	0	0	0	0	1	0	1	1	0	0	1	0
iroD	0	0	0	0	0	0	0	1	0	1	1	0	0	1	0
iroE	0	0	0	0	0	0	0	1	0	1	1	0	0	1	0
iroN	0	0	0	0	0	0	1	1	0	1	1	0	0	1	0
irp1	1	0	0	0	1	0	1	1	0	1	0	1	1	1	0
irp2	1	0	0	0	1	0	1	1	0	1	0	1	1	1	0
iucA	0	0	0	1	1	0	0	1	0	1	1	0	0	1	0
iucB	0	0	0	1	1	0	0	1	0	1	1	0	0	1	0
iucC	0	0	0	1	1	0	0	1	0	1	1	0	0	1	0
iucD	0	0	0	1	0	0	0	1	0	1	1	0	0	1	0
iutA	0	0	0	1	1	0	0	1	1	1	1	0	0	1	0
kpsD	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
kpsM	0	0	1	0	0	0	0	1	0	1	0	0	0	0	0
kpsT	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
lpfA	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
lpfB	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
lpfC	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
lpfE	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
map	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
mgtB	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
nleB2	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
ompA	1	1	1	0	1	1	1	1	0	1	0	1	1	0	0
paa	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0
papA	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
papB	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0
papC	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0
papD	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0
papE	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
papF	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0
papG	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0
papH	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0
papI	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0
papJ	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0
papK	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0
papX	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0
pic	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sat	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0
senB	0	0	0	0	1	0	0	0	1	0	1	0	0	0	0
sepD	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sepL	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sepQ/escQ	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sfaA	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sfaB	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sfaC	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sfaD	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sfaE	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sfaF	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sfaG	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sfaH	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sfaS	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sfaX	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sfaY	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
shuA	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
shuS	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
shuT	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
shuX	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
shuY	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
sinH	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
tcpC	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
tviB	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
tviC	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
tviD	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
tviE	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
vat	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
vexA	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
vexB	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
vexC	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
vexD	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
vexE	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
yagV/ecpE	0	0	0	1	0	0	0	1	1	1	0	0	1	0	0
yagW/ecpD	0	0	0	1	0	0	1	1	1	1	0	0	1	0	0
yagX/ecpC	0	0	0	1	0	0	0	1	1	1	0	0	1	0	0
yagY/ecpB	0	0	0	1	0	0	0	1	0	1	0	0	1	0	0
yagZ/ecpA	0	0	0	1	0	0	1	1	1	1	0	1	1	0	0
ybtA	1	0	1	0	1	0	1	1	0	1	0	1	1	1	0
ybtE	1	0	0	0	1	0	1	1	0	1	0	1	1	1	0
ybtP	1	0	1	0	1	0	1	1	0	1	0	1	1	1	0
ybtQ	1	0	1	0	1	0	1	1	0	1	0	1	1	1	0
ybtS	1	0	0	0	1	0	1	1	0	1	0	1	1	1	0
ybtT	1	0	0	0	1	0	1	1	0	1	0	1	1	1	0
ybtU	1	0	0	0	1	0	1	1	0	1	0	1	1	1	0
ybtX	1	0	1	0	1	0	1	1	0	1	0	1	1	1	0
ykgK/ecpR	0	0	0	1	0	0	0	1	1	1	0	0	1	0	0

VF Genes UpsetR

#----------------Step3: UpsetR Plot VFGenes

abr_upsetR_df <- Abricate_vf_df %>% select(VF_GENE,Species_ContigClass)
#View(abr_upsetR_df)

abr_upsetR_lt <- split(abr_upsetR_df$VF_GENE, abr_upsetR_df$Species_ContigClass) 
abr_comb_mat = make_comb_mat(abr_upsetR_lt)

col_size = comb_size(abr_comb_mat)
row_size = set_size(abr_comb_mat)

vf_ht = UpSet(abr_comb_mat, 
           row_title = "Species#SeqClassification", column_title = "Virulence factor genes Intersection across species",
           pt_size = unit(6, "pt"),
           lwd = unit(2, "pt"),
           #comb_col = "red",
           # bg_col = c("#d6b23b","#81dce2","#acf49a","#a784d8","#acf49a","#81dce2","#1a51dd","#1d7a01","#683cd8","#d62c4c","#683cd8","#1d7a01" ,
           #            "#dd71c0","#1a51dd","#dd71c0","#d62c4c"),
           #bg_col = upset_row_col$Color,
           top_annotation = upset_top_annotation(abr_comb_mat, gp = gpar(fill = "#009797"), add_numbers = FALSE, 
                                                 bar_width = 0.5, annotation_name_rot = 90),
           right_annotation = upset_right_annotation(abr_comb_mat, add_numbers = TRUE, gp = gpar(fill = "#009797")),
           show_row_names = TRUE,
           row_names_gp = gpar(fontsize = 9), #changes font size of "set size" labels
           width = unit(900, units = "pt"), height = unit(12, "cm"))

vf_ht = draw(vf_ht)
#ht
col_od = column_order(vf_ht)
row_od = row_order(vf_ht)

decorate_annotation("intersection_size", {
  grid.text(col_size[col_od], 
            seq_len(length(col_size)), 
            unit(col_size[col_od], "native") + unit(2, "mm"), 
            default.units = "native", just = "bottom",
            gp = gpar(fontsize = 8))
})

Table4

VF Genes proportions in Chromsome and plasmids by Species

vf_geneCounts <- Abricate_vf_df %>% 
  separate(Species_ContigClass, sep = "#", into = c("Species", "ContigClass")) %>%
  select(Species,VF_GENE,ContigClass) %>% 
  unique() %>% 
  mutate(Species_VF_GENE=paste(Species, VF_GENE, sep = '#')) %>% 
  select(Species_VF_GENE,ContigClass) %>% 
  table() %>% as.data.frame.matrix() %>% 
  mutate(BothChr_AND_Plasmid = case_when(Chromosome == 1 & Plasmid == 1 ~ 1, TRUE ~ 0)) %>% 
  tibble::rownames_to_column("Species#VFgenes") %>% 
  separate("Species#VFgenes", sep = "#", into = c("Species", "VFGene")) %>% 
  group_by(Species) %>% 
  summarise(Count_VFgenes_in_Chr_old=sum(Chromosome), Count_VFgenes_in_Plasmid_old=sum(Plasmid), Count_VFgenes_in_Chr_Plasmid=sum(BothChr_AND_Plasmid)) %>% 
  mutate(Count_VFgenes_in_Chr = Count_VFgenes_in_Chr_old - Count_VFgenes_in_Chr_Plasmid, 
         Count_VFgenes_in_Plasmid = Count_VFgenes_in_Plasmid_old - Count_VFgenes_in_Chr_Plasmid) %>% 
  select(Species,Count_VFgenes_in_Chr,Count_VFgenes_in_Plasmid,Count_VFgenes_in_Chr_Plasmid) %>% 
  column_to_rownames(var="Species")

# divides each cell value with corresponding row sum value
vf_geneCounts_props <- vf_geneCounts*100/rowSums(vf_geneCounts) 

vf_geneCounts_props %>% 
  kbl(caption = "VF Genes proportions in Chromsome and plasmids by Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

VF Genes proportions in Chromsome and plasmids by Species

	Count_VFgenes_in_Chr	Count_VFgenes_in_Plasmid	Count_VFgenes_in_Chr_Plasmid
Citrobacter amalonaticus	100.00000	0.000000	0.000000
Citrobacter farmeri	100.00000	0.000000	0.000000
Citrobacter freundii	86.74699	13.253012	0.000000
Citrobacter koseri	100.00000	0.000000	0.000000
Citrobacter rodentium	100.00000	0.000000	0.000000
Enterobacter aerogenes	100.00000	0.000000	0.000000
Enterobacter cloacae	91.01124	2.247191	6.741573
Escherichia coli	80.71066	4.060914	15.228426
Klebsiella oxytoca	100.00000	0.000000	0.000000
Klebsiella pneumoniae	37.14286	28.571429	34.285714
Serratia marcescens	100.00000	0.000000	0.000000

Plot2

VF Genes Percentages in Chromsome and plasmids by Species

vf_geneCounts_props %>% 
  rownames_to_column(var="Species") %>% 
  reshape2::melt(variable = "VF Gene location", value.name = "Percentage") %>% 
  na_if(0) %>% 
  ggplot(aes(x = Species, y=Percentage, fill = `VF Gene location`, label = Percentage)) + 
  geom_bar(width = 0.4, position = position_stack(), stat = "identity")+
  scale_fill_manual(labels=c('Chromosome', 'Plasmid',"Both"), values = c("#fcb6b1", "#facc96", "#4dbfbc")) +
  geom_text(aes(label=paste0(sprintf("%1.1f", Percentage),"%")), position=position_stack(vjust=0.5)) +
  scale_x_discrete(expand = c(0,0)) +
  ylab('Percentage')+
  xlab('Species')+
  ggtitle('Percentages of Virulence factor genes by their location')+
  theme_classic() +
  theme(axis.text.y=element_text(size=16,  vjust = 0, hjust = 1),
        axis.text.x=element_text(size=16, angle=90, vjust = 0, hjust = 1.2),
        axis.line=element_blank(),
        axis.ticks=element_blank(),
        legend.position="top") + coord_flip()

Insertion Sequence (IS)

Table1

Percentages of Samples carrying Insertion Sequences in Chromsomes and Plasmids across Species

###@@@@@@@@@@@@@@ Dataframe6 - Prokka Insertion Sequences 

Prokka_IS_df <- data.table::fread(file = "6_Prokka_InsertionSequences/CPE_Trans_Prokka_IS.tab", sep = "\t", header = TRUE)
#head(Prokka_IS_df)

colnames(Prokka_IS_df) <- c("Fasta", "Contig", "Fasta_Contig", "IS")

#----------------Step2: Combining plasClass dataframe with Prokka_IS dataframe for plotting Transposons

Prokka_IS_plasClass_df <- left_join(Prokka_IS_df, plasClass_Prob_df, by = c("Fasta_Contig")) %>% 
  select(Fasta.x,Contig.x,Fasta_Contig,IS,Classification,Species_ContigClass)

#head(Prokka_IS_plasClass_df)

Prokka_IS_plasClass_df <- Prokka_IS_plasClass_df %>% filter(Fasta.x %in% hybridAssemb_1198list) # Prokka_IS_plasClass_df now has only 1198 assembly information

# Prokka_IS_plasClass_df %>%
#   select(Transposon,Species_ContigClass) %>%
#   table() %>% # frequency of unique transposons
#   kbl(caption = "Transposons and their counts in Chromosomes and Plasmids across Species") %>%
#   kable_classic(full_width = F, html_font = "Cambria")

customGreen0 = "#DeF7E9"
customRed = "#ff7f7f"
Prokka_IS_plasClass_df_total_col = Prokka_IS_plasClass_df %>% 
  select(Fasta.x,IS,Species_ContigClass) %>% 
  unique() %>%
  select(Species_ContigClass) %>% 
  unique() %>% 
  count() %>% c()


# percentages of IS (https://clarewest.github.io/blog/post/making-tables-shiny/)
Prokka_IS_plasClass_df %>%
  select(Fasta.x,IS,Species_ContigClass) %>%
  unique() %>%
  select(IS,Species_ContigClass) %>%
  count(IS, Species_ContigClass) %>%
  rename(Freq = n) %>%
  drop_na(Species_ContigClass) %>%
  mutate(Species_ContigClass2=Species_ContigClass) %>%
  separate(Species_ContigClass2, sep = "#", into = c("Species", "ContigClass")) %>%
  left_join(Species_Counts, by = c("Species")) %>%
  mutate(SamplePrcnt_wGeneElement=Freq*100/SampleCount) %>%
  select(IS,Species_ContigClass,SamplePrcnt_wGeneElement) %>%
  data.table::dcast(IS~Species_ContigClass, value.var="SamplePrcnt_wGeneElement") %>%
  replace(is.na(.), 0) %>%
  mutate_if(is.numeric, round, digits=1) %>% 
  formattable(., align = c("l",rep("c", Prokka_IS_plasClass_df_total_col$n)), 
              list(
                `IS` = formatter("span", style = ~ style(color = "grey",font.weight = "bold")), 
                area(col = 1:Prokka_IS_plasClass_df_total_col$n+1) ~ color_tile(customGreen0, customRed))) %>% 
  as.datatable(escape = FALSE,
               options = list(scrollX = FALSE, dom = 'lfti',
                              lengthMenu = list(c(5, 10, 25, 100, -1), c('5','10','25', '100', 'All')),
                              pageLength = 5),
               rownames = FALSE)

Table2

Insertion Sequences and their unique counts in Chromsomes and Plasmids across Species

Prokka_IS_plasClass_df %>% 
  select(IS,Species_ContigClass) %>% 
  unique() %>% 
  table() %>% # frequency of unique transposons
  kbl(caption = "Insertion Sequence and their unique counts in Chromsomes and Plasmids across Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

Insertion Sequence and their unique counts in Chromsomes and Plasmids across Species

	Citrobacter amalonaticus#Chromosome	Citrobacter amalonaticus#Plasmid	Citrobacter farmeri#Chromosome	Citrobacter farmeri#Plasmid	Citrobacter freundii#Chromosome	Citrobacter freundii#Plasmid	Citrobacter koseri#Chromosome	Citrobacter koseri#Plasmid	Citrobacter rodentium#Chromosome	Citrobacter rodentium#Plasmid	Enterobacter aerogenes#Chromosome	Enterobacter aerogenes#Plasmid	Enterobacter cloacae#Chromosome	Enterobacter cloacae#Plasmid	Escherichia coli#Chromosome	Escherichia coli#Plasmid	Klebsiella oxytoca#Chromosome	Klebsiella oxytoca#Plasmid	Klebsiella pneumoniae#Chromosome	Klebsiella pneumoniae#Plasmid	Morganella morganii#Chromosome	Morganella morganii#Plasmid	Proteus mirabilis#Chromosome	Proteus mirabilis#Plasmid	Serratia marcescens#Chromosome	Serratia marcescens#Plasmid
IS100	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	0	1	1	0	0	0	0	0	0
IS1006	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0
IS100kyp	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	0	1	1	0	0	0	0	0	0
IS10A	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	1	0	0	0	1	1	0	0	0	0	0
IS10R	0	0	0	0	0	1	0	0	0	1	1	1	1	1	1	1	0	0	1	1	1	0	1	0	0	0
IS1133	0	0	0	0	0	1	0	0	0	0	0	0	1	1	1	1	0	0	0	1	0	0	0	0	0	0
IS1203	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0	0	0	0	0	0
IS1222	0	0	0	0	0	1	0	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0	0
IS1230B	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0
IS1247	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
IS1271	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
IS1294	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
IS1326	0	0	0	0	1	1	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0
IS1351	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
IS1353	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
IS1394	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
IS1396	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0
IS1397	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	1	0	0	0	0	0	0	0
IS1400	1	0	0	0	0	0	1	0	0	0	1	1	1	0	0	0	0	0	1	1	0	0	0	0	0	0
IS1414	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	1	1	0	0	0	0	0	0
IS1489v1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
IS1491	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
IS15	0	0	0	0	0	1	0	0	0	0	0	1	0	1	1	1	0	1	1	1	0	0	0	0	0	0
IS150	0	0	0	0	1	0	0	0	0	0	0	0	1	1	1	1	0	0	0	0	0	0	0	0	0	0
IS1541B	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
IS15DI	0	1	0	0	1	1	0	1	0	1	0	1	1	1	1	1	0	1	1	1	0	0	1	0	0	0
IS15DII	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	0	0	1	1	0	0	0	0	0	0
IS15DIV	1	1	0	1	1	1	0	1	1	1	1	1	1	1	1	1	0	1	1	1	1	1	1	0	0	0
IS1618	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
IS186A	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
IS186B	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
IS1A	0	0	0	0	1	1	0	0	0	0	0	1	1	1	1	1	0	1	1	1	0	0	0	0	0	0
IS1B	0	0	0	0	1	1	0	1	1	1	0	1	1	1	1	1	0	1	1	1	1	1	1	0	0	0
IS1D	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	1	0	0	0	0	0	0
IS1F	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0
IS1G	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	0	0	1	1	0	0	0	0	0	0
IS1H	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
IS1N	1	0	0	0	0	0	0	0	0	0	1	0	1	1	1	1	0	1	1	1	0	0	0	0	0	0
IS1S	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	0	1	1	0	0	0	0	0	0
IS1SD	0	0	0	0	1	1	0	0	0	0	0	1	1	0	1	1	0	1	1	1	0	0	0	0	0	0
IS1X1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	1	1	0	0	0	0	0	0
IS1X2	1	1	0	1	1	1	0	1	1	1	0	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0
IS1X3	0	1	0	0	1	1	0	1	1	1	0	0	1	1	1	1	1	1	1	1	0	0	0	0	0	0
IS1X4	1	0	0	0	1	1	0	0	0	1	0	1	1	1	1	1	1	0	1	1	0	0	0	0	0	0
IS2	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	1	0	1	1	0	0	0	0	0	0
IS200C	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0
IS200F	0	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0	0
IS21	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	0	0	0	0	0	0	0	0	0	0
IS231L	1	0	1	0	0	0	0	0	1	0	0	0	1	0	1	0	1	0	1	0	1	0	0	0	0	0
IS285	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0
IS3	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	0	0	0	1	0	0	0	0	0	0
IS3000	0	0	0	0	1	1	0	0	0	0	1	1	1	1	1	1	0	1	1	1	0	0	0	0	0	0
IS30D	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	1	1	1	1	0	0	0	0	0	0
IS30H	0	0	0	0	1	1	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0
IS3411	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	1	0	0	0
IS3F	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
IS3H	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	0	0	1	1	0	0	0	0	0	0
IS4	1	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	1	0	0	0	0	0	0	0	0	0
IS406	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0	0
IS421	0	0	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	1	1	0	0	0	0	0	0
IS4321	1	1	0	0	1	1	0	0	0	0	0	0	1	1	0	1	0	0	1	1	0	0	0	0	0	0
IS4321R	1	1	0	0	1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0
IS4811	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0
IS5075	1	1	0	0	1	1	0	0	0	1	0	1	1	1	1	1	1	1	1	1	0	0	0	0	0	1
IS50R	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0
IS5564	1	0	1	0	1	0	0	0	0	0	1	0	1	0	1	0	1	0	1	1	0	0	0	0	1	0
IS5708	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	0	1	1	1	0	0	0	0	0	0
IS5B	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0	0	0	0	0	0
IS5D	1	0	0	0	1	1	0	0	1	1	0	0	1	1	1	1	1	1	1	1	0	0	1	0	0	0
IS606	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
IS609	0	0	0	0	1	1	0	0	0	0	0	0	1	0	1	1	0	0	1	1	0	0	1	0	0	0
IS6100	0	1	0	1	0	1	0	1	1	1	0	1	1	1	1	1	0	1	1	1	1	1	0	0	0	1
IS621	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	0	1	0	0	0	0	0	0	0
IS629	0	0	0	0	0	1	0	0	0	0	0	0	1	1	1	1	0	0	1	1	0	0	1	0	0	0
IS630	1	0	0	0	0	0	0	0	0	0	1	1	1	0	1	1	0	0	1	0	0	0	0	0	0	0
IS679	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0	0
IS682	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	1	1	1	0	0	0	0	0	0
IS884	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
IS903	0	0	0	0	1	1	0	1	0	1	0	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0
IS903B	1	1	0	0	1	1	0	1	0	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0
IS91	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
IS911	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	0	0	0	0	0	0	0	0	0	0
ISAav1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	1	0	0	1	1	0	0	0	0	0	0
ISAba1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0
ISAba125	0	1	0	1	0	1	0	1	0	0	0	1	1	1	1	1	0	1	0	1	0	1	0	0	0	0
ISAba14	0	0	0	0	0	1	0	0	0	0	0	0	1	1	1	1	0	0	0	1	0	0	0	0	0	0
ISAba33	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0
ISAba42	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
ISAba43	0	0	0	0	1	0	0	0	0	0	0	1	1	1	1	1	0	0	1	1	0	0	1	0	0	0
ISAba8	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
ISAbe14	1	0	1	0	1	0	0	0	0	0	0	0	1	1	1	1	0	0	1	1	0	0	1	0	0	0
ISAcba1	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	1	1	0	1	1	0	0	0	0	0	0
ISAeca1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0
ISAeme15	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0	0	0	0
ISAeme19	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0
ISAeme9	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISAfe13	0	1	0	1	1	1	0	1	0	1	0	1	1	1	0	1	0	1	1	1	0	1	0	0	0	0
ISAlde2	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	1
ISAlw16	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
ISApl1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISApu1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0
ISApu2	0	0	0	0	1	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0
ISArsp9	0	0	0	0	1	1	0	0	0	1	0	0	1	1	1	1	0	1	1	1	1	1	0	0	0	0
ISAs17	0	0	0	0	1	1	0	0	0	0	1	1	0	1	0	1	0	0	1	1	0	0	0	0	0	0
ISAs19	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
ISAs25	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0
ISAs29	0	0	0	0	1	1	0	0	0	0	0	0	1	1	0	0	0	0	1	1	0	0	0	0	0	0
ISAs9	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISAzs36	0	0	0	0	1	1	0	0	0	0	0	1	1	1	0	1	0	0	0	1	0	0	0	0	0	0
ISBcen15	0	0	0	0	1	0	0	0	0	0	0	1	1	0	1	0	0	0	1	1	0	0	0	0	0	0
ISBdi12	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISBli29	0	1	0	1	1	1	0	1	0	1	0	1	1	1	1	1	0	1	1	1	1	1	1	1	0	0
ISBli6	0	0	0	0	1	1	0	0	0	0	0	0	0	0	1	1	0	0	1	1	1	0	1	0	0	0
ISBmo1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0
ISBmu20	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0
ISBos1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISBrsa1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISBsp7	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	1	0	0	0	0	0
ISBth5	1	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0
ISCARN93	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISCco2	0	0	0	0	0	1	0	0	0	0	0	0	1	1	1	1	0	0	0	1	0	0	0	0	0	0
ISCco3	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0
ISCep1	1	0	1	0	1	0	1	0	1	0	1	0	1	0	1	1	1	0	1	0	1	0	1	0	1	0
ISCfr1	0	0	0	0	1	1	0	1	0	1	0	0	1	1	1	1	0	1	1	1	1	0	1	0	0	0
ISCfr12	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	1	1	0	0	0	0	0	0
ISCfr13	1	0	0	0	1	0	0	0	0	0	1	0	1	1	0	0	0	0	1	1	0	0	0	0	0	0
ISCfr14	0	0	0	0	1	1	0	0	0	0	0	1	1	1	0	1	0	0	1	1	0	0	0	0	0	0
ISCfr26	0	0	0	0	1	1	0	0	0	0	0	0	1	1	0	1	0	0	0	1	0	0	0	0	0	0
ISCfr3	0	0	0	0	1	1	0	1	0	0	0	0	0	1	1	1	0	0	0	1	0	0	0	0	0	0
ISCfr4	0	0	0	0	1	1	0	0	0	0	1	1	0	0	1	0	0	0	1	1	0	0	0	0	0	0
ISCfr6	0	0	0	0	0	0	0	0	0	1	1	0	1	1	1	1	0	0	1	1	0	0	0	0	0	0
ISCfr8	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0
ISCgl1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1	0
ISCot1	1	0	0	0	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	0	1	0	0	0	1
ISCro1	1	0	0	0	1	0	0	0	0	0	0	0	0	1	1	1	0	0	1	1	0	0	0	0	0	0
ISCro2	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISCro3	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISCro4	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0	0	0
ISCro5	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISCro6	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISCte8	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	1
ISDsh3	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0
ISEae1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0
ISEal1	0	0	0	0	1	1	0	0	0	0	0	1	1	1	0	0	1	1	1	1	1	0	0	0	0	0
ISEam1	1	0	1	0	1	1	0	0	0	0	1	0	1	1	1	0	0	0	1	1	0	0	0	0	0	0
ISEas1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISEc1	0	0	0	0	0	0	0	0	1	0	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0
ISEc10	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	1	1	1	1	1	0	0	0	0	0	0
ISEc11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
ISEc12	0	0	0	0	1	1	0	0	0	0	0	0	1	0	1	1	0	0	1	1	1	0	0	0	0	0
ISEc13	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0	0
ISEc14	1	0	0	0	1	1	0	0	0	0	0	1	1	1	1	1	0	0	1	1	0	0	0	0	0	0
ISEc15	0	0	0	0	1	1	0	0	0	1	0	0	1	1	0	1	1	0	1	1	0	0	0	0	0	0
ISEc16	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0
ISEc17	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	0	1	1	1	0	0	0	0	0	0
ISEc18	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISEc19	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISEc20	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0
ISEc21	1	0	0	0	1	1	0	0	0	0	0	0	1	0	1	1	0	1	1	1	0	0	0	0	0	0
ISEc22	0	0	0	0	1	0	0	0	0	0	0	0	0	1	1	1	0	0	1	1	0	0	0	0	0	0
ISEc23	0	0	0	0	0	1	0	0	0	1	0	0	1	1	1	1	0	0	1	1	0	0	1	0	0	0
ISEc24	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	1	0	1	0	0	0	0	0	0	0
ISEc25	0	0	0	0	1	1	0	0	0	0	0	0	1	1	0	1	0	0	1	1	1	0	0	0	0	0
ISEc26	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISEc27	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	1	1	1	1	0	0	0	0	0	0
ISEc29	0	0	0	0	0	1	0	1	0	0	0	1	0	1	1	1	0	1	1	1	0	0	0	0	0	0
ISEc30	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISEc31	0	0	0	0	1	0	0	0	0	0	1	0	1	1	1	1	0	0	1	1	0	0	0	0	0	0
ISEc32	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0
ISEc33	0	1	0	1	1	1	0	1	0	1	0	1	1	1	1	1	0	1	1	1	0	1	0	0	0	0
ISEc35	0	0	0	0	1	1	0	1	0	0	0	1	1	1	1	1	0	1	1	1	0	0	0	0	0	0
ISEc36	1	0	0	0	1	1	0	0	0	0	0	1	1	1	1	1	1	0	1	1	0	0	0	0	0	0
ISEc37	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0
ISEc38	0	0	0	0	1	1	0	0	0	0	0	1	1	1	1	1	0	1	1	1	1	0	0	0	0	0
ISEc39	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	0	1	0	0	0	0	0	0	0
ISEc40	0	0	0	0	1	1	0	0	0	0	0	0	0	1	1	0	1	1	1	1	0	0	0	0	0	0
ISEc41	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0
ISEc42	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	1	1	0	0	1	0	0	0	0	0	0
ISEc43	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0
ISEc44	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISEc45	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISEc46	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0	0
ISEc47	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0	0
ISEc48	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISEc49	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISEc5	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISEc52	1	0	0	0	1	1	0	0	0	0	1	1	1	1	1	1	0	1	1	1	1	0	1	0	0	0
ISEc53	1	0	1	0	1	0	0	0	0	0	1	0	1	0	1	1	0	0	1	0	1	0	1	0	0	0
ISEc59	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0
ISEc60	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0
ISEc62	0	0	0	0	1	1	0	0	0	0	0	1	1	1	1	1	0	0	0	1	0	0	0	0	0	0
ISEc63	0	1	0	0	1	1	0	0	0	0	0	0	1	1	1	1	0	1	0	1	0	1	0	0	0	0
ISEc66	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISEc68	0	0	0	1	1	0	0	0	0	0	0	0	1	1	1	1	0	0	1	1	0	0	0	0	0	0
ISEc76	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0	0	0	0	0
ISEc77	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0
ISEc78	1	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	0	0	1	1	0	0	0	0	0	0
ISEc8	1	0	0	0	1	1	0	0	0	0	0	1	1	1	1	1	0	0	1	1	0	0	0	0	0	0
ISEc81	0	0	0	0	0	1	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0
ISEc83	1	0	0	0	1	0	0	0	1	0	0	0	1	0	1	1	0	0	1	0	0	0	0	0	0	0
ISEc84	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
ISEc86	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISEc9	1	1	0	0	1	1	0	1	0	1	0	1	1	1	1	1	0	1	1	1	1	0	1	0	0	0
ISEcB1	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISEch11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISEch12	1	0	1	0	1	0	0	0	0	0	1	1	0	0	1	0	0	0	1	1	0	0	0	0	0	0
ISEch14	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0
ISEch2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISEch6	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISEcl1	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	1	1	1	1	0	0	0	0	0	0
ISEcl10	1	0	1	0	1	0	1	0	0	0	0	0	1	0	1	1	0	0	1	0	1	0	0	0	0	0
ISEcl11	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISEcl3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	1	0	0	0
ISEcl6	0	0	0	0	1	0	1	0	0	0	0	1	1	1	1	0	1	1	1	1	0	0	0	0	0	0
ISEhe2	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0
ISEhe3	1	0	0	0	1	1	1	0	1	0	1	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0
ISEhe4	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	1	1	0	0	0	0	0	0
ISEic2	0	0	0	0	1	0	1	0	0	0	0	0	0	1	1	1	0	0	1	1	0	0	0	0	0	0
ISEisp1	0	0	0	0	1	0	0	0	0	0	1	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0
ISErsp1	1	0	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	0	1	0	0	0	0	0	0	0
ISEsa1	0	0	0	0	1	1	0	0	0	1	0	0	1	1	1	0	0	1	1	1	0	0	0	0	0	0
ISEsa2	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	1	1	1	1	0	0	0	0	0	0
ISEsp1	0	0	0	0	1	1	1	0	0	0	0	0	1	1	1	0	1	1	1	1	0	0	0	0	0	0
ISGme8	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISHce1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISHgi14	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISHne2	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISHp1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISIde1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
ISKox1	0	0	0	0	0	1	0	0	0	0	1	0	1	1	1	0	0	0	1	1	0	0	0	0	0	0
ISKox2	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0
ISKox3	0	1	0	0	1	1	0	0	1	1	1	0	1	1	1	1	0	0	1	1	0	0	0	0	0	0
ISKpn1	1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0
ISKpn11	0	1	0	0	1	1	0	0	0	1	0	1	1	1	1	1	0	1	1	1	0	0	0	0	0	0
ISKpn12	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0
ISKpn14	0	0	0	0	1	1	0	1	0	0	0	1	1	1	1	1	0	1	1	1	1	0	0	0	0	0
ISKpn15	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISKpn18	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0	0	0	0
ISKpn19	1	0	0	0	1	1	0	0	0	0	0	1	0	1	1	1	0	0	1	1	0	0	0	0	0	0
ISKpn2	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	1	0	1	1	0	0	0	0	0	0
ISKpn20	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	0
ISKpn21	1	1	0	0	1	1	0	0	0	0	1	1	1	1	1	1	1	1	1	1	0	0	0	0	1	0
ISKpn24	0	0	0	0	1	1	0	0	1	1	1	1	1	1	1	1	0	1	1	1	0	0	0	0	0	0
ISKpn25	1	0	1	0	1	1	0	0	0	0	1	1	1	1	1	1	0	1	1	1	0	0	1	0	0	0
ISKpn26	1	1	0	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	0	0	1	0	0	0
ISKpn27	0	1	0	1	1	1	0	1	0	0	0	1	0	1	1	1	0	1	1	1	0	1	0	0	0	0
ISKpn28	0	0	0	0	0	1	0	0	0	0	0	1	0	1	1	1	1	1	1	1	0	0	0	0	0	0
ISKpn3	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISKpn31	1	0	0	0	1	1	0	0	0	1	0	1	1	1	1	1	0	1	1	1	1	0	1	0	0	0
ISKpn33	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0	1	1	1	1	0	0	0	0	0	0
ISKpn34	1	0	0	0	0	1	0	0	0	0	0	0	1	1	0	1	0	0	1	1	0	0	0	0	1	0
ISKpn37	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0
ISKpn38	0	0	0	0	1	1	0	0	0	0	0	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0
ISKpn4	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0
ISKpn40	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISKpn41	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0
ISKpn42	0	0	0	0	1	0	0	0	0	0	0	0	0	1	1	1	0	0	1	1	0	0	0	0	0	0
ISKpn43	0	1	0	0	1	0	0	0	0	0	0	0	0	1	0	1	0	1	1	1	0	0	0	0	0	0
ISKpn47	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	1	0	0	0	0	0	0
ISKpn49	0	0	0	0	1	1	0	0	1	0	0	0	1	1	1	0	0	1	1	1	0	0	0	0	0	0
ISKpn50	1	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	1	0	0	0	0	0	0
ISKpn53	1	0	0	0	1	0	1	0	1	0	1	1	1	0	1	1	0	0	1	1	0	0	0	0	0	0
ISKpn54	1	0	1	0	1	0	0	0	0	0	1	1	1	0	1	0	1	0	1	1	1	0	0	0	0	0
ISKpn6	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0
ISKpn60	0	0	0	0	1	1	0	0	0	1	0	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0
ISKpn66	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISKpn7	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISKpn8	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	1	1	1	1	0	0	0	0	1	0
ISKqu3	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	1	0	1	1	1	0	0	0	0	0	0
ISLad1	1	0	0	0	1	1	0	0	1	0	1	1	1	1	1	1	0	1	1	1	0	0	0	0	0	0
ISLad2	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	1	1	1	1	0	0	0	0	0	0
ISLad6	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	1	1	1	0	1	0	0	0	0	0	0
ISLead2	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	1	0	0	1	1	0	0	0	0	0	0
ISLead3	1	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISLpn8	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISMaq2	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	1	0	0	0	0	0	0
ISMasp6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
ISMph1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0
ISMtsp21	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	0	0
ISNisp5	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0	0
ISNpu13	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISPa16	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0
ISPa21	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISPa31	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISPa38	0	1	0	0	1	1	0	0	0	1	0	1	1	1	1	1	0	1	1	1	0	1	0	0	0	0
ISPa7	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
ISPa77	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISPa80	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISPa83	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISPa85	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISPa95	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	0	0	0	1	0	0	0	0	0	0
ISPa97	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0	0
ISPa99	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	1	0	0	0	0	0	0
ISPan1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	1	1	0	0	0	0	0	0
ISPcc1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISPcc2	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISPcc4	1	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISPcc6	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0
ISPeat2	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	0	0	0	1	1	0	0	0	0	0	0
ISPlge2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0
ISPlge3	0	0	0	0	0	1	0	0	0	1	0	0	1	1	0	0	0	0	1	1	0	0	0	0	0	0
ISPlge4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISPlu15	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0
ISPlu22	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	0	0	0
ISPlu8	0	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISPmi3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISPpr10	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISPps1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0
ISPpu1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISPpu12	0	0	0	0	1	1	0	0	0	0	0	0	1	0	1	1	0	0	0	1	0	0	0	0	0	0
ISPpu22	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISPpu23	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISPpu30	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISPre2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISPrre1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	1	0	1	0	0	0
ISPrre10	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISPrst3	1	1	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	1	0	1	0	0	0
ISPst2	0	0	0	0	1	1	0	0	0	0	0	0	0	1	1	1	0	0	1	1	0	0	0	0	0	0
ISPst3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
ISPst4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISPst8	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISPst9	0	0	0	0	1	1	0	0	0	0	0	0	1	0	1	0	0	0	0	1	0	0	0	0	0	0
ISPsy30	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	0	1	0	0	0	0	0	0
ISPsy42	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	1	1	0	1	0	0	0	0
ISPsy43	0	0	0	0	0	1	0	0	0	0	0	1	1	1	0	1	0	1	1	1	0	0	0	0	0	0
ISPye16	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
ISRaq1	0	0	0	0	1	1	1	1	0	0	0	0	1	1	0	1	1	0	1	1	0	0	0	0	1	0
ISRhba1	0	0	0	0	1	0	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0	0	0	0	0
ISRop1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	1	1	1	0	0	0	0	0	0
ISRor2	1	0	1	0	1	1	1	0	1	0	1	0	1	1	1	0	1	1	1	1	0	0	1	0	0	0
ISRor3	1	0	1	0	1	1	1	0	1	0	1	1	1	1	1	0	1	1	1	1	0	0	0	0	0	0
ISRosp3	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0
ISRso16	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	0	0	0	0	1	0	0	0	0	0	0
ISRso21	0	0	0	0	1	0	1	0	0	0	1	0	1	0	1	0	0	0	1	0	1	0	1	0	0	0
ISSaen1	1	0	0	0	0	0	0	0	1	0	0	0	1	0	1	0	1	0	1	1	1	0	1	0	0	0
ISSav7	1	0	1	0	1	0	1	0	1	0	1	0	1	0	1	0	1	0	1	0	1	0	1	0	0	0
ISSba1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
ISSba14	0	0	0	0	1	0	0	0	0	0	0	1	1	1	1	1	0	0	0	1	0	0	0	0	0	0
ISSba3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
ISSba8	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISSbo1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0	0	0	0	0	0
ISSd1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0
ISSde4	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	0	0
ISSde5	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISSen1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISSen10	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	1	1	1	1	0	0	0	0	0	0	0
ISSen13	0	0	0	0	1	0	0	0	0	0	0	0	0	1	1	1	1	0	1	1	0	0	0	0	0	0
ISSen3	1	1	0	0	1	1	0	0	0	0	0	0	1	1	1	1	0	0	1	1	0	0	0	0	0	0
ISSen4	0	1	0	1	1	1	0	1	1	1	0	1	1	1	1	1	0	1	1	1	0	1	0	0	0	0
ISSen6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	0	0	0	0	0
ISSen7	1	0	0	0	1	1	0	0	0	0	0	0	0	1	1	0	1	1	1	1	0	0	0	0	0	0
ISSen8	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0
ISSen9	0	0	0	0	0	1	0	0	0	0	0	0	1	1	0	1	0	1	1	1	0	0	0	0	0	0
ISSfl1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	1	0	0	0	0	0	0	0	0	0
ISSfl10	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	0	0	0	0	0	0	0	0
ISSfl3	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISSfl8	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISSfl9	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0	0	0
ISSgsp1	0	0	0	0	1	1	0	0	0	0	0	1	1	1	1	1	1	0	1	1	0	0	0	0	0	0
ISShdy1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
ISShdy2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISShes11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0
ISShes4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0
ISShfr8	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISShfr9	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0
ISShwo2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0
ISSlo2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0
ISSm1	1	1	0	0	1	1	0	0	0	0	0	0	1	1	0	0	0	1	0	1	0	0	0	0	0	0
ISSm3	1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0	0	0	0
ISSm4	0	1	0	0	1	1	1	0	0	0	1	0	1	1	1	1	0	0	1	1	0	0	1	0	0	0
ISSod16	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
ISSod23	1	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	1	0	1	0	0	0	0	0	0	0
ISSod4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0
ISSod6	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISSoEn2	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0	0	0	0	0	0	0
ISSoEn3	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	1	1
ISSpe2	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISSpr1	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISSpr2	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISSpu11	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISSpu2	0	0	0	0	0	1	0	0	0	0	0	0	1	1	1	1	0	0	1	1	0	0	0	0	0	0
ISSpu20	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0
ISSpu6	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	1	1	1	1	0	0	0	0
ISSpu7	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISSso4	0	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0	0	0	0	1	1
ISSsu9	1	1	1	1	1	1	1	0	1	0	1	1	1	1	1	1	1	0	1	1	1	0	1	0	0	0
ISStma11	1	0	1	0	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0	1	1
ISSty2	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	1	0	1	1	0	0	0	0	0	0
ISSysp7	0	0	0	0	1	0	0	0	0	0	1	0	1	1	1	0	1	0	1	1	0	1	0	0	0	0
ISTfu1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISThi1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	1	0	0	1	1	0	0	0	0	0	0
ISThsp9	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISUnCu16	0	0	0	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0
ISVch3	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0	0	0	1
ISVch4	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0
ISVch9	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
ISVsa17	0	0	0	0	1	0	0	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0	0	0
ISVsa3	0	0	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	1	1	0	0	0	0	0	0
ISWpi13	0	0	0	0	1	1	0	0	0	0	0	0	1	1	1	0	0	1	0	1	0	0	0	0	0	0
ISXc4	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	1	0	0	1	1	0	0	0	0	0	0
ISXne1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
ISYal1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	0
ISYen3	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0
ISYpe1	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
ISYps1	0	0	0	0	1	1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0
ISYps3	1	0	0	0	1	1	1	0	0	0	1	1	1	1	1	1	1	1	1	1	0	0	0	0	0	0
ISYps8	1	0	0	0	1	0	0	0	0	0	0	0	0	0	1	1	0	0	0	0	1	0	0	0	0	0
ISYru1	0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	1	0	0	0	0	0	0	0
Tn2	1	1	0	1	1	1	0	1	0	1	0	1	1	1	1	1	0	1	1	1	1	1	1	1	0	0
Tn3	0	1	0	0	0	1	0	0	0	0	0	0	0	1	1	1	0	0	1	1	0	1	0	0	0	0
Tn4430	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0
Tn5393	0	0	0	0	0	1	0	1	0	1	0	0	1	1	1	1	0	1	1	1	0	0	0	0	0	0
Tn5403	0	1	0	1	1	1	0	1	0	1	0	1	1	1	1	1	0	1	1	1	0	1	0	0	0	0
TnAs1	0	1	0	0	1	1	0	0	1	1	0	1	1	1	1	1	0	1	1	1	0	0	0	0	0	0
TnAs2	0	0	0	0	1	1	0	0	0	0	0	1	1	1	1	1	1	0	1	1	0	1	0	0	0	1
TnAs3	0	1	0	0	1	1	0	0	0	0	0	0	1	1	1	1	0	0	1	1	1	0	0	0	0	0
TnEc1	0	0	0	0	0	1	0	0	1	0	1	1	1	1	1	1	0	1	1	1	0	0	0	0	0	0
TnShfr1	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0	1	0	0	1	1	0	1	0	0	0	0
TnXax1	0	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	0	0	1	0	0	0	1	0

Insertion Sequences UpsetR

#----------------Step3: UpsetR Plot VFGenes

IS_upsetR_df <- Prokka_IS_plasClass_df  %>% select(IS,Species_ContigClass)
#View(IS_upsetR_df)

IS_upsetR_lt <- split(IS_upsetR_df$IS, IS_upsetR_df$Species_ContigClass) 
IS_comb_mat = make_comb_mat(IS_upsetR_lt)

col_size = comb_size(IS_comb_mat)
row_size = set_size(IS_comb_mat)

IS_ht = UpSet(IS_comb_mat, 
           row_title = "Species#SeqClassification", column_title = "Insertion Sequences Intersection across species",
           pt_size = unit(6, "pt"),
           lwd = unit(2, "pt"),
           #comb_col = "red",
           # bg_col = c("#d6b23b","#81dce2","#acf49a","#a784d8","#acf49a","#81dce2","#1a51dd","#1d7a01","#683cd8","#d62c4c","#683cd8","#1d7a01" ,
           #            "#dd71c0","#1a51dd","#dd71c0","#d62c4c"),
           #bg_col = upset_row_col$Color,
           top_annotation = upset_top_annotation(IS_comb_mat, gp = gpar(fill = "#009797"), add_numbers = FALSE, 
                                                 bar_width = 0.5, annotation_name_rot = 90),
           right_annotation = upset_right_annotation(IS_comb_mat, add_numbers = TRUE, gp = gpar(fill = "#009797")),
           show_row_names = TRUE,
           row_names_gp = gpar(fontsize = 9), #changes font size of "set size" labels
           width = unit(900, units = "pt"), height = unit(12, "cm"))

IS_ht = draw(IS_ht)
#ht
col_od = column_order(IS_ht)
row_od = row_order(IS_ht)

decorate_annotation("intersection_size", {
  grid.text(col_size[col_od], 
            seq_len(length(col_size)), 
            unit(col_size[col_od], "native") + unit(2, "mm"), 
            default.units = "native", just = "bottom",
            gp = gpar(fontsize = 8))
})

Table4

Insertion Sequences proportions in Chromsome and plasmids by Species

Prokka_ISCounts <- Prokka_IS_plasClass_df %>% 
  separate(Species_ContigClass, sep = "#", into = c("Species", "ContigClass")) %>%
  select(Species,IS,ContigClass) %>% 
  unique() %>% 
  mutate(Species_IS=paste(Species, IS, sep = '#')) %>% 
  select(Species_IS,ContigClass) %>% 
  table() %>% as.data.frame.matrix() %>% 
  mutate(BothChr_AND_Plasmid = case_when(Chromosome == 1 & Plasmid == 1 ~ 1, TRUE ~ 0)) %>% 
  tibble::rownames_to_column("Species#VFgenes") %>% 
  separate("Species#VFgenes", sep = "#", into = c("Species", "VFGene")) %>% 
  group_by(Species) %>% 
  summarise(Count_IS_in_Chr_old=sum(Chromosome), Count_IS_in_Plasmid_old=sum(Plasmid), Count_IS_in_Chr_Plasmid=sum(BothChr_AND_Plasmid)) %>% 
  mutate(Count_IS_in_Chr = Count_IS_in_Chr_old - Count_IS_in_Chr_Plasmid, 
         Count_IS_in_Plasmid = Count_IS_in_Plasmid_old - Count_IS_in_Chr_Plasmid) %>% 
  select(Species,Count_IS_in_Chr,Count_IS_in_Plasmid,Count_IS_in_Chr_Plasmid) %>% 
  column_to_rownames(var="Species")

# divides each cell value with corresponding row sum value
Prokka_ISCounts_props <- Prokka_ISCounts*100/rowSums(Prokka_ISCounts) 

Prokka_ISCounts_props %>% 
  kbl(caption = "Insertion Sequences proportions in Chromsome and plasmids by Species") %>% 
  kable_classic(full_width = F, html_font = "Cambria")

Insertion Sequences proportions in Chromsome and plasmids by Species

	Count_IS_in_Chr	Count_IS_in_Plasmid	Count_IS_in_Chr_Plasmid
Citrobacter amalonaticus	61.17647	22.352941	16.470588
Citrobacter farmeri	50.00000	46.428571	3.571429
Citrobacter freundii	29.38389	19.905213	50.710901
Citrobacter koseri	42.22222	55.555556	2.222222
Citrobacter rodentium	29.62963	44.444444	25.925926
Enterobacter aerogenes	22.77228	52.475248	24.752475
Enterobacter cloacae	31.03448	18.007663	50.957854
Escherichia coli	24.74916	15.719064	59.531773
Klebsiella oxytoca	27.19298	42.982456	29.824561
Klebsiella pneumoniae	12.28070	28.771930	58.947368
Morganella morganii	60.31746	26.984127	12.698413
Proteus mirabilis	89.74359	2.564103	7.692308
Serratia marcescens	47.36842	36.842105	15.789474

Plot2

Insertion Sequences Percentages in Chromsome and plasmids by Species

Prokka_ISCounts_props %>% 
  rownames_to_column(var="Species") %>% 
  reshape2::melt(variable = "Insertion Sequences location", value.name = "Percentage") %>% 
  na_if(0) %>% 
  ggplot(aes(x = Species, y=Percentage, fill = `Insertion Sequences location`, label = Percentage)) + 
  geom_bar(width = 0.4, position = position_stack(), stat = "identity")+
  scale_fill_manual(labels=c('Chromosome', 'Plasmid',"Both"), values = c("#fcb6b1", "#facc96", "#4dbfbc")) +
  geom_text(aes(label=paste0(sprintf("%1.1f", Percentage),"%")), position=position_stack(vjust=0.5)) +
  scale_x_discrete(expand = c(0,0)) +
  ylab('Percentage')+
  xlab('Species')+
  ggtitle('Percentages of Insertion Sequences by their location')+
  theme_classic() +
  theme(axis.text.y=element_text(size=16,  vjust = 0, hjust = 1),
        axis.text.x=element_text(size=16, angle=90, vjust = 0, hjust = 1.2),
        axis.line=element_blank(),
        axis.ticks=element_blank(),
        legend.position="top") + coord_flip()